manipulation.py

#   Copyright (c) 2022 PaddlePaddle Authors. All Rights Reserved.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
#     http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.

# TODO: define functions to manipulate a tensor

from collections import Counter

import numpy as np

import paddle
from paddle import _C_ops, _legacy_C_ops
from paddle.utils.inplace_utils import inplace_apis_in_dygraph_only

from ..common_ops_import import _varbase_creator, fill_constant
from ..fluid.data_feeder import (
    check_dtype,
    check_type,
    check_variable_and_dtype,
    convert_dtype,
)
from ..fluid.framework import _in_legacy_dygraph, _non_static_mode
from ..fluid.layers import utils
from ..framework import (
    LayerHelper,
    convert_np_dtype_to_dtype_,
    core,
    dygraph_only,
    in_dygraph_mode,
)
from ..static import Variable
from .creation import _complex_to_real_dtype, _real_to_complex_dtype, zeros

__all__ = []


def tensor_array_to_tensor(input, axis=1, use_stack=False, name=None):
    r"""
    This function concatenates or stacks all tensors in the input LoDTensorArray
    along the axis mentioned and returns that as the output.

    For Example:

    .. code-block:: text

        Case 1:

            Given:

                input.data = {[[0.6, 0.1, 0.3],
                               [0.5, 0.3, 0.2]],
                              [[1.3],
                               [1.8]],
                              [[2.3, 2.1],
                               [2.5, 2.4]]}

                axis = 1, use_stack = False

            Then:

                output.data = [[0.6, 0.1, 0.3, 1.3, 2.3, 2.1],
                               [0.5, 0.3, 0.2, 1.8, 2.5, 2.4]]

                output_index.data = [3, 1, 2]

        Case 2:

            Given:

                input.data = {[[0.6, 0.1],
                               [0.5, 0.3]],
                              [[0.3, 1.3],
                               [0.2, 1.8]],
                              [[2.3, 2.1],
                               [2.5, 2.4]]}

                axis = 1, use_stack = True

            Then:

                output.data = [[[0.6, 0.1]
                                [0.3, 1.3]
                                [2.3, 2.1],
                               [[0.5, 0.3]
                                [0.2, 1.8]
                                [2.5, 2.4]]]

                output_index.data = [2, 2, 2]

    Args:
        input(TensorArray): A TensorArray variable.
        axis(int): The axis along which the tensors in attr::`input` will be
            concatenated or stacked.
        use_stack(bool): Act as concat_op or stack_op. For stack mode, all
            tensors in the tensor array must have the same shape.
        name(str|None): A name for this layer(optional). If set None, the layer
                       will be named automatically.

    Returns:
        Tensor: The concatenated or stacked tensor variable.
        Tensor: A 1-D tensor variable with int32 data type. The data in this \
            tensor contains all input including tensors' sizes along the axis.

    Examples:
        .. code-block:: python

            import numpy
            import paddle
            x0 = paddle.assign(numpy.random.rand(2, 2).astype("float32"))
            x1 = paddle.assign(numpy.random.rand(2, 2).astype("float32"))
            i = paddle.full(shape=[1], dtype="int64", fill_value=0)
            array = paddle.tensor.array.create_array(dtype='float32')
            paddle.tensor.array.array_write(x0, i, array)
            paddle.tensor.array.array_write(x1, i + 1, array)
            output, output_index = paddle.tensor.manipulation.tensor_array_to_tensor(input=array)
    """
    if _non_static_mode():
        assert isinstance(
            input, list
        ), "The 'input' in tensor_array_to_tensor must be list"
        from paddle import concat, stack

        op = stack if use_stack else concat
        res = op(input, axis=axis)
        sizes = paddle.to_tensor(
            np.array(list(map(lambda x: int(x.shape[axis]), input)))
        )
        return res, sizes

    check_type(input, 'input', (list, Variable), 'tensor_array_to_tensor')
    if isinstance(input, list):
        for i, input_x in enumerate(input):
            check_type(
                input_x,
                'input[' + str(i) + ']',
                Variable,
                'tensor_array_to_tensor',
            )
    helper = LayerHelper('tensor_array_to_tensor', **locals())
    out = helper.create_variable_for_type_inference(dtype=helper.input_dtype())
    out_index = helper.create_variable_for_type_inference(dtype="int32")
    helper.append_op(
        type='tensor_array_to_tensor',
        inputs={'X': input},
        outputs={'Out': [out], 'OutIndex': [out_index]},
        attrs={'axis': axis, 'use_stack': use_stack},
    )
    return out, out_index


def cast(x, dtype):
    """

    Take in the Tensor :attr:`x` with :attr:`x.dtype` and cast it
    to the output with :attr:`dtype`. It's meaningless if the output dtype
    equals the input dtype, but it's fine if you do so.

    Args:
        x (Tensor): An input N-D Tensor with data type bool, float16,
            float32, float64, int32, int64, uint8.
        dtype (np.dtype|str): Data type of the output:
            bool, float16, float32, float64, int8, int32, int64, uint8.

    Returns:
        Tensor, A Tensor with the same shape as input's.

    Examples:
        .. code-block:: python

            import paddle

            x = paddle.to_tensor([2, 3, 4], 'float64')
            y = paddle.cast(x, 'uint8')
    """
    if in_dygraph_mode():
        if not isinstance(dtype, core.VarDesc.VarType):
            dtype = convert_np_dtype_to_dtype_(dtype)
        return _C_ops.cast(x, dtype)

    if _non_static_mode():
        if not isinstance(dtype, core.VarDesc.VarType):
            dtype = convert_np_dtype_to_dtype_(dtype)
        out = _legacy_C_ops.cast(x, 'in_dtype', x.dtype, 'out_dtype', dtype)
        return out

    check_variable_and_dtype(
        x,
        'x',
        [
            'bool',
            'float16',
            'float32',
            'float64',
            'int16',
            'int32',
            'int64',
            'uint8',
            'uint16',
        ],
        'cast',
    )
    check_dtype(
        dtype,
        'dtype',
        [
            'bool',
            'float16',
            'float32',
            'float64',
            'int8',
            'int16',
            'int32',
            'int64',
            'uint8',
            'uint16',
        ],
        'cast',
    )

    helper = LayerHelper('cast', **locals())
    out = helper.create_variable_for_type_inference(
        dtype=dtype, stop_gradient=x.stop_gradient
    )
    helper.append_op(
        type='cast',
        inputs={'X': [x]},
        outputs={'Out': [out]},
        attrs={'in_dtype': x.dtype, 'out_dtype': out.dtype},
    )
    return out


def slice(input, axes, starts, ends):
    """
    This operator produces a slice of ``input`` along multiple axes. Similar to numpy:
    https://docs.scipy.org/doc/numpy/reference/arrays.indexing.html
    Slice uses ``axes``, ``starts`` and ``ends`` attributes to specify the start and
    end dimension for each axis in the list of axes and Slice uses this information
    to slice the input data tensor. If a negative value is passed to
    ``starts`` or ``ends`` such as :math:`-i`,  it represents the reverse position of the
    axis :math:`i-1` (here 0 is the initial position).
    If the value passed to ``starts`` or ``ends`` is greater than n
    (the number of elements in this dimension), it represents n.
    For slicing to the end of a dimension with unknown size, it is recommended
    to pass in INT_MAX. The size of ``axes`` must be equal to ``starts`` and ``ends``.
    Following examples will explain how slice works:

    .. code-block:: text

        Case1:
            Given:
                data = [ [1, 2, 3, 4], [5, 6, 7, 8], ]
                axes = [0, 1]
                starts = [1, 0]
                ends = [2, 3]
            Then:
                result = [ [5, 6, 7], ]

        Case2:
            Given:
                data = [ [1, 2, 3, 4], [5, 6, 7, 8], ]
                axes = [0, 1]
                starts = [0, 1]
                ends = [-1, 1000]       # -1 denotes the reverse 0th position of dimension 0.
            Then:
                result = [ [2, 3, 4], ] # result = data[0:1, 1:4]

    Args:
        input (Tensor): A ``Tensor`` . The data type is ``float16``, ``float32``, ``float64``, ``int32`` or ``int64``.
        axes (list|tuple): The data type is ``int32`` . Axes that `starts` and `ends` apply to .
        starts (list|tuple|Tensor): The data type is ``int32`` . If ``starts`` is a list or tuple, the elements of
                it should be integers or Tensors with shape [1]. If ``starts`` is an Tensor, it should be an 1-D Tensor.
                It represents starting indices of corresponding axis in ``axes``.
        ends (list|tuple|Tensor): The data type is ``int32`` . If ``ends`` is a list or tuple, the elements of
                it should be integers or Tensors with shape [1]. If ``ends`` is an Tensor, it should be an 1-D Tensor .
                It represents ending indices of corresponding axis in ``axes``.

    Returns:
        Tensor, A ``Tensor``. The data type is same as ``input``.

    Examples:
        .. code-block:: python

            import paddle

            input = paddle.rand(shape=[4, 5, 6], dtype='float32')
            # example 1:
            # attr starts is a list which doesn't contain tensor.
            axes = [0, 1, 2]
            starts = [-3, 0, 2]
            ends = [3, 2, 4]
            sliced_1 = paddle.slice(input, axes=axes, starts=starts, ends=ends)
            # sliced_1 is input[1:3, 0:2, 2:4].

            # example 2:
            # attr starts is a list which contain tensor.
            minus_3 = paddle.full([1], -3, "int32")
            sliced_2 = paddle.slice(input, axes=axes, starts=[minus_3, 0, 2], ends=ends)
            # sliced_2 is input[1:3, 0:2, 2:4].
    """
    if in_dygraph_mode():
        attrs = ()
        starts_tensor = None
        ends_tensor = None

        if isinstance(axes, (list, tuple)):
            axes = list(axes)
            if len(axes) == 0:
                raise ValueError(
                    "Input axes should not be an empty list/tuple."
                )
            for i in range(len(axes)):
                if axes[i] < 0:
                    axes[i] = max(0, axes[i] + len(input.shape))
                else:
                    axes[i] = min(len(input.shape) - 1, axes[i])

        else:
            raise ValueError(
                "Input axes must be a python list or tuple, but reveived {}".format(
                    type(axes)
                )
            )

        infer_flags = list(1 for i in range(len(axes)))

        tmp_tensor_type = core.eager.Tensor

        if isinstance(starts, (list, tuple)):
            starts = [
                item.numpy().item(0)
                if isinstance(item, tmp_tensor_type)
                else item
                for item in starts
            ]
        elif isinstance(starts, tmp_tensor_type):
            tensor_t = starts.numpy()
            starts = [ele for ele in tensor_t]
            infer_flags = list(-1 for i in range(len(axes)))

        if isinstance(ends, (list, tuple)):
            ends = [
                item.numpy().item(0)
                if isinstance(item, tmp_tensor_type)
                else item
                for item in ends
            ]
        elif isinstance(ends, tmp_tensor_type):
            tensor_t = ends.numpy()
            ends = [ele for ele in tensor_t]
            infer_flags = list(-1 for i in range(len(axes)))

        return _C_ops.slice(input, axes, starts, ends, infer_flags, [])
    else:
        if _in_legacy_dygraph():
            attrs = ()
            starts_tensor = None
            ends_tensor = None

            if isinstance(axes, (list, tuple)):
                axes = list(axes)
                if len(axes) == 0:
                    raise ValueError(
                        "Input axes should not be an empty list/tuple."
                    )
                for i in range(len(axes)):
                    if axes[i] < 0:
                        axes[i] = max(0, axes[i] + len(input.shape))
                    else:
                        axes[i] = min(len(input.shape) - 1, axes[i])

            else:
                raise ValueError(
                    "Input axes must be a python list or tuple, but reveived {}".format(
                        type(axes)
                    )
                )

            infer_flags = list(1 for i in range(len(axes)))

            tmp_tensor_type = Variable

            if isinstance(starts, (list, tuple)):
                starts = [
                    item.numpy().item(0)
                    if isinstance(item, tmp_tensor_type)
                    else item
                    for item in starts
                ]
                attrs += ('starts', starts)
            elif isinstance(starts, tmp_tensor_type):
                starts_tensor = starts
                starts.stop_gradient = True
                infer_flags = list(-1 for i in range(len(axes)))

            if isinstance(ends, (list, tuple)):
                ends = [
                    item.numpy().item(0)
                    if isinstance(item, tmp_tensor_type)
                    else item
                    for item in ends
                ]
                attrs += ('ends', ends)
            elif isinstance(ends, tmp_tensor_type):
                ends_tensor = ends
                ends_tensor.stop_gradient = True
                infer_flags = list(-1 for i in range(len(axes)))

            return _legacy_C_ops.slice(
                input,
                starts_tensor,
                ends_tensor,
                None,
                None,
                'axes',
                axes,
                'infer_flags',
                infer_flags,
                *attrs,
            )

    if not isinstance(starts, (list, tuple, Variable)):
        raise ValueError(
            "Input starts must be an Variable, python list or tuple."
        )
    if not isinstance(ends, (list, tuple, Variable)):
        raise ValueError(
            "Input ends must be an Variable, python list or tuple."
        )

    helper = LayerHelper('slice', **locals())

    inputs = {'Input': input}
    attrs = {'axes': axes}
    infer_flags = list(1 for i in range(len(axes)))

    # starts
    if isinstance(starts, Variable):
        starts.stop_gradient = True
        inputs['StartsTensor'] = starts
        infer_flags = list(-1 for i in range(len(axes)))
    elif isinstance(starts, (list, tuple)):
        attrs['starts'] = []
        if utils._contain_var(starts):
            inputs['StartsTensorList'] = utils._convert_to_tensor_list(starts)
            for i, dim in enumerate(starts):
                if isinstance(dim, Variable):
                    attrs['starts'].append(-1)
                    infer_flags[i] = -1
                else:
                    attrs['starts'].append(dim)
        else:
            attrs['starts'] = starts

    # ends
    if isinstance(ends, Variable):
        ends.stop_gradient = True
        inputs['EndsTensor'] = ends
        infer_flags = list(-1 for i in range(len(axes)))
    elif isinstance(ends, (list, tuple)):
        attrs['ends'] = []
        if utils._contain_var(ends):
            inputs['EndsTensorList'] = utils._convert_to_tensor_list(ends)
            for i, dim in enumerate(ends):
                if isinstance(dim, Variable):
                    attrs['ends'].append(-1)
                    infer_flags[i] = -1
                else:
                    attrs['ends'].append(dim)
        else:
            attrs['ends'] = ends

    # infer_flags
    attrs['infer_flags'] = infer_flags
    out = helper.create_variable_for_type_inference(
        dtype=helper.input_dtype('input')
    )
    helper.append_op(
        type='slice', inputs=inputs, attrs=attrs, outputs={'Out': out}
    )

    return out


def transpose(x, perm, name=None):
    """
    Permute the data dimensions of `input` according to `perm`.

    The `i`-th dimension  of the returned tensor will correspond to the
    perm[i]-th dimension of `input`.

    Args:
        x (Tensor): The input Tensor. It is a N-D Tensor of data types bool, float32, float64, int32.
        perm (list|tuple): Permute the input according to the data of perm.
        name (str): The name of this layer. It is optional.

    Returns:
        Tensor, A transposed n-D Tensor, with data type being bool, float32, float64, int32, int64.

    For Example:

        .. code-block:: text

         x = [[[ 1  2  3  4] [ 5  6  7  8] [ 9 10 11 12]]
             [[13 14 15 16] [17 18 19 20] [21 22 23 24]]]
         shape(x) =  [2,3,4]

         # Example 1
         perm0 = [1,0,2]
         y_perm0 = [[[ 1  2  3  4] [13 14 15 16]]
                   [[ 5  6  7  8]  [17 18 19 20]]
                   [[ 9 10 11 12]  [21 22 23 24]]]
         shape(y_perm0) = [3,2,4]

         # Example 2
         perm1 = [2,1,0]
         y_perm1 = [[[ 1 13] [ 5 17] [ 9 21]]
                   [[ 2 14] [ 6 18] [10 22]]
                   [[ 3 15]  [ 7 19]  [11 23]]
                   [[ 4 16]  [ 8 20]  [12 24]]]
         shape(y_perm1) = [4,3,2]

    Examples:

        .. code-block:: python

            import paddle

            x = paddle.randn([2, 3, 4])
            x_transposed = paddle.transpose(x, perm=[1, 0, 2])
            print(x_transposed.shape)
            # [3L, 2L, 4L]

    """
    if in_dygraph_mode():
        return _C_ops.transpose(x, perm)
    else:
        if _in_legacy_dygraph():
            out, _ = _legacy_C_ops.transpose2(x, 'axis', perm)
            return out

    check_variable_and_dtype(
        x,
        'x',
        [
            'bool',
            'float16',
            'float32',
            'float64',
            'int32',
            'int64',
            'complex64',
            'complex128',
        ],
        'transpose',
    )
    check_type(perm, 'perm', (list, tuple), 'transpose')
    if isinstance(perm, tuple):
        perm = list(perm)
    if len(perm) != len(x.shape):
        raise ValueError(
            "Input(perm) is the permutation of dimensions of Input(x), "
            "its length should be equal to dimensions of Input(x), "
            "but received dimension of Input(x) is %s, "
            "the length of Input(perm) is %s." % (len(x.shape), len(perm))
        )
    for idx, dim in enumerate(perm):
        if dim >= len(x.shape):
            raise ValueError(
                "Each element in Input(perm) should be less than Input(x)'s dimension, "
                "but %d-th element in Input(perm) is %d which exceeds Input(x)'s "
                "dimension %d." % (idx, perm[idx], len(x.shape))
            )

    helper = LayerHelper('transpose', **locals())
    out = helper.create_variable_for_type_inference(x.dtype)
    x_shape = helper.create_variable_for_type_inference(x.dtype)
    helper.append_op(
        type='transpose2',
        inputs={'X': [x]},
        outputs={'Out': [out], 'XShape': [x_shape]},
        attrs={'axis': perm},
    )
    return out


def unstack(x, axis=0, num=None):
    """
    This layer unstacks input Tensor :code:`x` into several Tensors along :code:`axis`.

    If :code:`axis` < 0, it would be replaced with :code:`axis+rank(x)`.
    If :code:`num` is None, it would be inferred from :code:`x.shape[axis]`,
    and if :code:`x.shape[axis]` <= 0 or is unknown, :code:`ValueError` is
    raised.

    Args:
        x (Tensor): Input Tensor. It is a N-D Tensors of data types float32, float64, int32, int64.
        axis (int): The axis along which the input is unstacked.
        num (int|None): The number of output variables.

    Returns:
        list(Tensor), The unstacked Tensors list. The list elements are N-D Tensors of data types float32, float64, int32, int64.

    Examples:
        .. code-block:: python

            import paddle
            x = paddle.ones(name='x', shape=[2, 3, 5], dtype='float32')  # create a tensor with shape=[2, 3, 5]
            y = paddle.unstack(x, axis=1)  # unstack with second axis, which results 3 tensors with shape=[2, 5]

    """
    if in_dygraph_mode():
        if num is None:
            num = x.shape[axis]
        if num == 0:
            return []
        return _C_ops.unstack(x, axis, num)

    if _non_static_mode():
        if num is None:
            num = x.shape[axis]
        if num == 0:
            return []
        return _legacy_C_ops.unstack(x, num, 'axis', int(axis), 'num', num)

    helper = LayerHelper('unstack', **locals())
    if num is None:
        if axis is None or x.shape[axis] <= 0:
            raise ValueError('unknown unstack number')
        else:
            num = x.shape[axis]

    outs = []
    for _ in range(num):
        outs.append(helper.create_variable_for_type_inference(x.dtype))

    helper.append_op(
        type='unstack',
        inputs={'X': [x]},
        outputs={'Y': outs},
        attrs={'axis': axis, 'num': num},
    )
    return outs


def shard_index(input, index_num, nshards, shard_id, ignore_value=-1):
    """
    Reset the values of `input` according to the shard it beloning to.
    Every value in `input` must be a non-negative integer, and
    the parameter `index_num` represents the integer above the maximum
    value of `input`. Thus, all values in `input` must be in the range
    [0, index_num) and each value can be regarded as the offset to the beginning
    of the range. The range is further split into multiple shards. Specifically,
    we first compute the `shard_size` according to the following formula,
    which represents the number of integers each shard can hold. So for the
    i'th shard, it can hold values in the range [i*shard_size, (i+1)*shard_size).
    ::

        shard_size = (index_num + nshards - 1) // nshards

    For each value `v` in `input`, we reset it to a new value according to the
    following formula:
    ::

        v = v - shard_id * shard_size if shard_id * shard_size <= v < (shard_id+1) * shard_size else ignore_value

    That is, the value `v` is set to the new offset within the range represented by the shard `shard_id`
    if it in the range. Otherwise, we reset it to be `ignore_value`.

    Args:
        input (Tensor): Input tensor with data type int64 or int32. It's last dimension must be 1.
        index_num (int): An integer represents the integer above the maximum value of `input`.
        nshards (int): The number of shards.
        shard_id (int): The index of the current shard.
        ignore_value (int): An integer value out of sharded index range.

    Returns:
        Tensor.

    Examples:
        .. code-block:: python

            import paddle
            label = paddle.to_tensor([[16], [1]], "int64")
            shard_label = paddle.shard_index(input=label,
                                             index_num=20,
                                             nshards=2,
                                             shard_id=0)
            print(shard_label)
            # [[-1], [1]]
    """
    if in_dygraph_mode():
        return _C_ops.shard_index(
            input, index_num, nshards, shard_id, ignore_value
        )

    check_variable_and_dtype(input, 'input', ['int64', 'int32'], 'shard_index')
    op_type = 'shard_index'
    helper = LayerHelper(op_type, **locals())
    if shard_id < 0 or shard_id >= nshards:
        raise ValueError(
            'The shard_id(%d) should be in [0, %d)' % (shard_id, nshards)
        )

    out = helper.create_variable_for_type_inference(dtype=input.dtype)
    helper.append_op(
        type=op_type,
        inputs={'X': [input]},
        outputs={'Out': out},
        attrs={
            'index_num': index_num,
            'nshards': nshards,
            'shard_id': shard_id,
            'ignore_value': ignore_value,
        },
        stop_gradient=True,
    )
    return out


def crop(x, shape=None, offsets=None, name=None):
    """
    Crop input into output, as specified by offsets and shape.

    .. code-block:: text

        * Case 1 (input is a 2-D Tensor):
            Input:
                X.shape = [3, 5]
                X.data = [[0, 1, 2, 0, 0],
                          [0, 3, 4, 0, 0],
                          [0, 0, 0, 0, 0]]
            Parameters:
                shape = [2, 2]
                offsets = [0, 1]
            Output:
                Out.shape = [2, 2]
                Out.data = [[1, 2],
                            [3, 4]]
        * Case 2 (input is a 3-D Tensor):
            Input:
                X.shape = [2, 3, 4]
                X.data =  [[[0, 1, 2, 3],
                            [0, 5, 6, 7],
                            [0, 0, 0, 0]],
                           [[0, 3, 4, 5],
                            [0, 6, 7, 8],
                            [0, 0, 0, 0]]]
            Parameters:
                shape = [2, 2, -1]
                offsets = [0, 0, 1]
            Output:
                Out.shape = [2, 2, 3]
                Out.data  = [[[1, 2, 3],
                              [5, 6, 7]],
                             [[3, 4, 5],
                              [6, 7, 8]]]

    Parameters:
        x (Tensor): 1-D to 6-D Tensor, the data type is float32, float64, int32 or int64.
        shape (list|tuple|Tensor, optional): The output shape is specified
            by `shape`. Its data type is int32. If a list/tuple, it's length must be
            the same as the dimension size of `x`. If a Tensor, it should be a 1-D Tensor.
            When it is a list, each element can be an integer or a Tensor of shape: [1].
            If Variable contained, it is suitable for the case that the shape may
            be changed each iteration.
        offsets (list|tuple|Variable, optional): Specifies the cropping
            offsets at each dimension. Its data type is int32. If a list/tuple, it's length
            must be the same as the dimension size of `x`. If a Tensor, it should be a 1-D
            Tensor. When it is a list, each element can be an integer or a Tensor of shape: [1].
            If Variable contained, it is suitable for the case that the offsets may be changed
            each iteration. Default: None, the offsets are 0 at each dimension.
        name (str, optional): Name for the operation (optional, default is None). For more information, please refer to :ref:`api_guide_Name`.

    Returns:
        Tensor, The cropped Tensor has same data type with `x`.

    Examples:

        .. code-block:: python

            import paddle
            x = paddle.to_tensor([[1, 2, 3], [4, 5, 6], [7, 8, 9]])
            # x.shape = [3, 3]
            # x = [[1, 2, 3], [4, 5, 6], [7, 8, 9]]

            # shape can be a 1-D Tensor or list or tuple.
            shape = paddle.to_tensor([2, 2], dtype='int32')
            # shape = [2, 2]
            # shape = (2, 2)
            out = paddle.crop(x, shape)
            # out.shape = [2, 2]
            # out = [[1,2], [4,5]]

            # offsets can be a 1-D Tensor or list or tuple.
            offsets = paddle.to_tensor([0, 1], dtype='int32')
            # offsets = [1, 0]
            # offsets = (1, 1)
            out = paddle.crop(x, shape, offsets)
            # out.shape = [2, 2]
            # if offsets = [0, 0], out = [[1,2], [4,5]]
            # if offsets = [0, 1], out = [[2,3], [5,6]]
            # if offsets = [1, 0], out = [[4,5], [7,8]]
            # if offsets = [1, 1], out = [[5,6], [8,9]]

    """

    helper = LayerHelper('crop_tensor', **locals())
    check_variable_and_dtype(
        x, 'x', ['float32', 'float64', 'int32', 'int64'], 'crop_tensor'
    )
    check_type(
        shape, 'shape', (list, tuple, Variable, type(None)), 'crop_tensor'
    )
    check_type(
        offsets, 'offsets', (list, tuple, Variable, type(None)), 'crop_tensor'
    )

    if offsets is None:
        offsets = [0] * len(x.shape)

    if shape is None:
        shape = x.shape

    if in_dygraph_mode():
        return _C_ops.crop(x, shape, offsets)

    out = helper.create_variable_for_type_inference(x.dtype)
    ipts = {'X': x}
    attrs = {}

    def _attr_shape_check(shape_val):
        if not isinstance(shape_val, int):
            raise TypeError(
                "Attr(shape)'s dtype of Op(crop_tensor) should be int32, but received: %s."
                % type(shape_val)
            )
        if shape_val == 0:
            raise ValueError(
                "Attr(shape) of Op(crop_tensor) should not be zero, but received: %s."
                % str(shape_val)
            )
        if shape_val < -1:
            raise ValueError(
                "When the element in Attr(shape) of Op(crop_tensor) is negative, only -1 is supported, but received: %s."
                % str(shape_val)
            )

    def _attr_offsets_check(offset_val):
        if not isinstance(offset_val, int):
            raise TypeError(
                "Attr(offsets)'s dtype of Op(crop_tensor) should be int32, but received: %s."
                % type(offset_val)
            )
        if offset_val < 0:
            raise ValueError(
                "Attr(offsets) of Op(crop_tensor) should be greater or equal to zero, but received: %s."
                % str(offset_val)
            )

    if isinstance(offsets, Variable):
        offsets.stop_gradient = True
        ipts['Offsets'] = offsets
        attrs['offsets'] = [-1] * len(x.shape)
    elif utils._contain_var(offsets):
        new_offsets_tensor = []
        offsets_attr = []
        for dim in offsets:
            if isinstance(dim, Variable):
                dim.stop_gradient = True
                new_offsets_tensor.append(dim)
                offsets_attr.append(-1)
            else:
                _attr_offsets_check(dim)
                temp_out = helper.create_variable_for_type_inference('int32')
                fill_constant([1], 'int32', dim, force_cpu=True, out=temp_out)
                new_offsets_tensor.append(temp_out)
                offsets_attr.append(dim)
        ipts['OffsetsTensor'] = new_offsets_tensor
        attrs['offsets'] = offsets_attr
    else:
        for offset in offsets:
            _attr_offsets_check(offset)
        attrs['offsets'] = offsets

    if isinstance(shape, Variable):
        shape.stop_gradient = True
        ipts['Shape'] = shape
    elif utils._contain_var(shape):
        new_shape_tensor = []
        shape_attr = []
        for dim_size in shape:
            if isinstance(dim_size, Variable):
                dim_size.stop_gradient = True
                new_shape_tensor.append(dim_size)
                shape_attr.append(0)
            else:
                _attr_shape_check(dim_size)
                temp_out = helper.create_variable_for_type_inference('int32')
                fill_constant(
                    [1], 'int32', dim_size, force_cpu=True, out=temp_out
                )
                new_shape_tensor.append(temp_out)
                shape_attr.append(dim_size)
        ipts['ShapeTensor'] = new_shape_tensor
        attrs['shape'] = shape_attr
    else:
        for dim_size in shape:
            _attr_shape_check(dim_size)
        attrs['shape'] = shape

    helper.append_op(
        type='crop_tensor',
        inputs=ipts,
        outputs={'Out': out},
        attrs=None if len(attrs) == 0 else attrs,
    )
    return out


@dygraph_only
def fill_(x, value):
    """
    **Notes**:
        **This API is ONLY available in Dygraph mode**

    This function fill the Tensor with value inplace.

    Args:
        x (Tensor): ``x`` is the Tensor we want to filled data inplace
        value (Scale): ``value`` is the value to be filled in x

    Returns:
        x(Tensor), Tensor x filled with value inplace

    Examples:
        .. code-block:: python

            import paddle

            tensor = paddle.to_tensor([0, 1, 2, 3, 4])

            tensor.fill_(0)
            print(tensor.tolist())   #[0, 0, 0, 0, 0]

    """
    if not isinstance(value, (float, int)):
        raise TypeError(
            "The type of 'value'  must be int or float, but received %s."
            % (type(value))
        )
    if in_dygraph_mode():
        return _C_ops.fill_(x, value)
    else:
        return _legacy_C_ops.fill_any_(
            x, "value_float", float(value), "value_int", int(value)
        )


@dygraph_only
def zero_(x):
    """
    **Notes**:
        **This API is ONLY available in Dygraph mode**

    This function fill the Tensor with zero inplace.

    Args:
        x (Tensor): ``x`` is the Tensor we want to filled with zero inplace

    Returns:
        x (Tensor), Tensor x filled with zero inplace

    Examples:
        .. code-block:: python

            import paddle

            tensor = paddle.to_tensor([0, 1, 2, 3, 4])

            tensor.zero_()
            print(tensor.tolist())   #[0, 0, 0, 0, 0]

    """
    if in_dygraph_mode():
        return _C_ops.fill_(x, 0.0)
    else:
        return _legacy_C_ops.fill_any_(
            x, "value_float", 0.0, "value_int", int(0)
        )


@dygraph_only
def fill_diagonal_(x, value, offset=0, wrap=False, name=None):
    """
    Note:
        This API is ONLY available in Dygraph mode.

    This function fill the value into the x Tensor's diagonal inplace.

    Args:
        x(Tensor): ``x`` is the original Tensor
        value(Scale): ``value`` is the value to filled in x
        offset(int,optional): the offset to the main diagonal. Default: 0 (main diagonal).
        wrap(bool,optional): the diagonal 'wrapped' after N columns for tall matrices.
        name(str,optional): Name for the operation (optional, default is None)

    Returns:
        Tensor, Tensor with diagonal filled with value.

    Examples:
        .. code-block:: python
            import paddle
            x = paddle.ones((4, 3)) * 2
            x.fill_diagonal_(1.0)
            print(x.tolist())   #[[1.0, 2.0, 2.0], [2.0, 1.0, 2.0], [2.0, 2.0, 1.0], [2.0, 2.0, 2.0]]
    """

    helper = LayerHelper("fill_diagonal_", **locals())
    check_type(x, 'X', (Variable), 'fill_diagonal_')
    dtype = helper.input_dtype('x')
    check_dtype(
        dtype,
        'X',
        ['bool', 'float16', 'float32', 'float64', 'int32', 'int64'],
        'fill_diagonal_',
    )
    check_type(value, 'value', (bool, int, float), 'fill_diagonal_')
    check_type(wrap, 'wrap', (bool), 'fill_diagonal_')

    inshape = x.shape
    inshapeset = set(inshape)
    assert len(inshape) >= 2, 'Tensor dims should >= 2 in fill_diagonal_ API'
    if len(inshape) > 2:
        assert (
            len(inshapeset) == 1
        ), 'Tensor dims should be equal while input dims > 2 in fill_diagonal_ API'
    if in_dygraph_mode():
        if len(inshape) == 2:
            return _C_ops.fill_diagonal_(x, value, offset, wrap)
        return _C_ops.fill_diagonal_(x, value, offset, True)

    if len(inshape) == 2:
        return _legacy_C_ops.fill_diagonal_(
            x, 'value', value, 'offset', offset, 'wrap', wrap
        )
    return _legacy_C_ops.fill_diagonal_(
        x, 'value', value, 'offset', offset, 'wrap', True
    )


def _fill_diagonal_tensor_impl(x, y, offset=0, dim1=0, dim2=1, inplace=False):
    inshape = x.shape
    assert dim1 < len(inshape) and dim1 >= -len(
        inshape
    ), 'dim1 should between [-rank,rank) in fill_diagonal_tensor_'
    assert dim2 < len(inshape) and dim2 >= -len(
        inshape
    ), 'dim2 should between [-rank,rank) in fill_diagonal_tensor_'
    assert len(inshape) >= 2, 'Tensor dims should >= 2 in fill_diagonal_tensor_'
    dim1 %= len(inshape)
    dim2 %= len(inshape)

    predshape = []
    for i in range(len(inshape)):
        if i != dim1 and i != dim2:
            predshape.append(inshape[i])
    diaglen = min(
        min(inshape[dim1], inshape[dim1] + offset),
        min(inshape[dim2], inshape[dim2] - offset),
    )
    predshape.append(diaglen)
    assert tuple(predshape) == tuple(
        y.shape
    ), "the y shape should be {}".format(predshape)
    if len(y.shape) == 1:
        y = y.reshape([1, -1])

    if inplace:
        if in_dygraph_mode():
            return _C_ops.fill_diagonal_tensor_(x, y, offset, dim1, dim2)
        else:
            return _legacy_C_ops.fill_diagonal_tensor_(
                x, y, 'offset', offset, 'dim1', dim1, 'dim2', dim2
            )
    if in_dygraph_mode():
        return _C_ops.fill_diagonal_tensor(x, y, offset, dim1, dim2)
    else:
        return _legacy_C_ops.fill_diagonal_tensor(
            x, y, 'offset', offset, 'dim1', dim1, 'dim2', dim2
        )


def fill_diagonal_tensor_(x, y, offset=0, dim1=0, dim2=1, name=None):
    """
    Note:
        This API is ONLY available in Dygraph mode.

    This function fill the source Tensor y into the x Tensor's diagonal inplace.

    Args:
        x (Tensor): ``x`` is the original Tensor
        y (Tensor): ``y`` is the Tensor to filled in x
        dim1 (int,optional): first dimension with respect to which to fill diagonal. Default: 0.
        dim2 (int,optional): second dimension with respect to which to fill diagonal. Default: 1.
        offset (int,optional): the offset to the main diagonal. Default: 0 (main diagonal).
        name (str, optional): Name for the operation (optional, default is None). For more information, please refer to :ref:`api_guide_Name`.

    Returns:
        Tensor, Tensor with diagonal filled with y.

    Examples:
        .. code-block:: python

            import paddle

            x = paddle.ones((4, 3)) * 2
            y = paddle.ones((3,))
            x.fill_diagonal_tensor_(y)
            print(x.tolist())   #[[1.0, 2.0, 2.0], [2.0, 1.0, 2.0], [2.0, 2.0, 1.0], [2.0, 2.0, 2.0]]

    """
    return _fill_diagonal_tensor_impl(
        x, y, offset=offset, dim1=dim1, dim2=dim2, inplace=True
    )


def fill_diagonal_tensor(x, y, offset=0, dim1=0, dim2=1, name=None):
    """
    This function fill the source Tensor y into the x Tensor's diagonal.

    Args:
        x (Tensor): ``x`` is the original Tensor
        y (Tensor): ``y`` is the Tensor to filled in x
        dim1 (int,optional): first dimension with respect to which to fill diagonal. Default: 0.
        dim2 (int,optional): second dimension with respect to which to fill diagonal. Default: 1.
        offset (int,optional): the offset to the main diagonal. Default: 0 (main diagonal).
        name (str, optional): Name for the operation (optional, default is None). For more information, please refer to :ref:`api_guide_Name`.

    Returns:
        Tensor, Tensor with diagonal filled with y.

    Examples:
        .. code-block:: python

            import paddle

            x = paddle.ones((4, 3)) * 2
            y = paddle.ones((3,))
            nx = x.fill_diagonal_tensor(y)
            print(nx.tolist())   #[[1.0, 2.0, 2.0], [2.0, 1.0, 2.0], [2.0, 2.0, 1.0], [2.0, 2.0, 2.0]]

    """
    return _fill_diagonal_tensor_impl(
        x, y, offset=offset, dim1=dim1, dim2=dim2, inplace=False
    )


@dygraph_only
def tolist(x):
    """
    Note:
        This API is ONLY available in Dygraph mode.

    This function translate the paddle.Tensor to python list.

    Args:
        x (Tensor): ``x`` is the Tensor we want to translate to list.

    Returns:
        list, A list that contain the same value of current Tensor.


    Examples:
        .. code-block:: python

            import paddle

            t = paddle.to_tensor([0,1,2,3,4])
            expectlist = t.tolist()
            print(expectlist)   #[0, 1, 2, 3, 4]

            expectlist = paddle.tolist(t)
            print(expectlist)   #[0, 1, 2, 3, 4]

    """
    return x.numpy().tolist()


def concat(x, axis=0, name=None):
    """

    Concatenates the input along the axis.

    Args:
        x (list|tuple): ``x`` is a Tensor list or Tensor tuple which is with data type bool, float16,
            float32, float64, int32, int64, int8, uint8. All the Tensors in ``x`` must have same data type.
        axis (int|Tensor, optional): Specify the axis to operate on the input Tensors.
            It's a scalar with data type int or a Tensor with shape [1] and data type int32
            or int64. The effective range is [-R, R), where R is Rank(x). When ``axis < 0``,
            it works the same way as ``axis+R``. Default is 0.
        name (str, optional): Name for the operation (optional, default is None). For more information, please refer to :ref:`api_guide_Name`.

    Returns:
        Tensor, A Tensor with the same data type as ``x``.

    Examples:
        .. code-block:: python

            import paddle

            x1 = paddle.to_tensor([[1, 2, 3],
                                   [4, 5, 6]])
            x2 = paddle.to_tensor([[11, 12, 13],
                                   [14, 15, 16]])
            x3 = paddle.to_tensor([[21, 22],
                                   [23, 24]])
            zero = paddle.full(shape=[1], dtype='int32', fill_value=0)
            # When the axis is negative, the real axis is (axis + Rank(x))
            # As follow, axis is -1, Rank(x) is 2, the real axis is 1
            out1 = paddle.concat(x=[x1, x2, x3], axis=-1)
            out2 = paddle.concat(x=[x1, x2], axis=0)
            out3 = paddle.concat(x=[x1, x2], axis=zero)
            # out1
            # [[ 1  2  3 11 12 13 21 22]
            #  [ 4  5  6 14 15 16 23 24]]
            # out2 out3
            # [[ 1  2  3]
            #  [ 4  5  6]
            #  [11 12 13]
            #  [14 15 16]]
    """
    input = x
    if in_dygraph_mode():
        if isinstance(axis, Variable):
            axis = axis.numpy()
            axis = axis.item(0)
        if not isinstance(input, Variable):
            input = [t for t in input if t.shape.count(0) == 0]
        return _C_ops.concat(input, axis)

    if _in_legacy_dygraph():
        if isinstance(axis, Variable):
            axis = axis.numpy()
            axis = axis.item(0)
        if not isinstance(input, Variable):
            input = [t for t in input if t.shape.count(0) == 0]
        out = _varbase_creator()
        _legacy_C_ops.concat(input, out, 'axis', axis)
        return out

    check_type(input, 'input', (list, tuple, Variable), 'concat')
    if not isinstance(input, Variable):
        for id, x in enumerate(input):
            check_variable_and_dtype(
                x,
                'input[' + str(id) + ']',
                [
                    'bool',
                    'float16',
                    'float32',
                    'float64',
                    'int32',
                    'int64',
                    'int8',
                    'unit8',
                ],
                'concat',
            )
            if x.dtype != input[0].dtype:
                raise TypeError(
                    "All the Tensors in the input must have the same data type."
                )
    else:
        input = [input]
    check_type(axis, 'axis', (int, Variable), 'concat')

    if isinstance(axis, Variable):
        check_dtype(
            axis.dtype,
            'axis',
            ['int32', 'int64'],
            'concat',
            "The data type of axis must be int32 or int64 when axis is a Tensor",
        )

    helper = LayerHelper('concat', **locals())
    out = helper.create_variable_for_type_inference(dtype=helper.input_dtype())

    if input[0].desc.type() == core.VarDesc.VarType.LOD_TENSOR_ARRAY:
        # NOTE(liym27): Don't remove this if branch!
        # This feature is supported for Dynamic-to-Static, because after transformed, the type of inputs[0]
        # is LOD_TENSOR_ARRAY in some scenarios. And this feature can be used in static mode.

        assert len(input) == 1, (
            "If the elements of 'input' in concat are Variable(LoDTensorArray), "
            "number of the elements must be 1, but received %s." % len(input)
        )
        out_index = helper.create_variable_for_type_inference(dtype="int32")
        helper.append_op(
            type='tensor_array_to_tensor',
            inputs={'X': input[0]},
            outputs={'Out': [out], 'OutIndex': [out_index]},
            attrs={'axis': axis, 'use_stack': False},
        )
    else:
        inputs = {'X': input}
        attrs = {}
        if isinstance(axis, Variable):
            axis.stop_gradient = True
            inputs['AxisTensor'] = axis
        else:
            attrs['axis'] = axis

        helper.append_op(
            type='concat', inputs=inputs, outputs={'Out': [out]}, attrs=attrs
        )
    return out


def broadcast_tensors(input, name=None):
    """
    Broadcast a list of tensors following broadcast semantics

    Note:
        If you want know more about broadcasting, please refer to `Introduction to Tensor`_ .

    .. _Introduction to Tensor: ../../guides/beginner/tensor_en.html#chapter5-broadcasting-of-tensor

    Args:
        input (list|tuple): ``input`` is a Tensor list or Tensor tuple which is with data type bool,
            float16, float32, float64, int32, int64. All the Tensors in ``input`` must have same data type.
            Currently we only support tensors with rank no greater than 5.
        name (str, optional): Name for the operation (optional, default is None). For more information, please refer to :ref:`api_guide_Name`.

    Returns:
        list(Tensor), The list of broadcasted tensors following the same order as ``input``.

    Examples:
        .. code-block:: python

            import paddle
            x1 = paddle.rand([1, 2, 3, 4]).astype('float32')
            x2 = paddle.rand([1, 2, 1, 4]).astype('float32')
            x3 = paddle.rand([1, 1, 3, 1]).astype('float32')
            out1, out2, out3 = paddle.broadcast_tensors(input=[x1, x2, x3])
            # out1, out2, out3: tensors broadcasted from x1, x2, x3 with shape [1,2,3,4]
    """

    num_inputs = len(input)
    if paddle.framework.in_dygraph_mode():
        return _C_ops.broadcast_tensors(input)
    if paddle.framework._non_static_mode():
        return _legacy_C_ops.broadcast_tensors(input, num_inputs)

    check_type(input, 'input', (list, tuple), 'broadcast_tensors')
    if num_inputs < 1:
        raise TypeError(
            "At least 1 tensor is needed to perform broadcast_tensors"
        )

    # Check input types
    for id, x in enumerate(input):
        check_variable_and_dtype(
            x,
            'input[' + str(id) + ']',
            ['bool', 'float32', 'float64', 'int32', 'int64'],
            'broadcast_tensors',
        )
        if x.dtype != input[0].dtype:
            raise TypeError(
                "All the Tensors in the input must have the same data type."
            )

    # Check bcast semantics
    output_shape_r_last_tensor_index = []
    output_shape_r = []

    # Use while loop due to weird behaviour of "range()"
    j = 0
    while j < len(input):
        tensor = input[j]
        shape = list(reversed(tensor.shape))

        i = 0
        while i < len(shape):
            if len(output_shape_r) <= i:
                output_shape_r.append(shape[i])
                output_shape_r_last_tensor_index.append(j)
            else:
                invalid = (
                    output_shape_r[i] != shape[i]
                    and output_shape_r[i] != 1
                    and shape[i] != 1
                )
                if invalid:
                    last_index = output_shape_r_last_tensor_index[i]
                    raise TypeError(
                        "Input tensors to broadcast_tensors does not follow bcast semantics"
                        "Tensor {last_index} conflicts with Tensor {j} in reversed dimension {i}"
                    )
                if output_shape_r[i] <= shape[i]:
                    output_shape_r[i] = shape[i]
                    output_shape_r_last_tensor_index[i] = j
            i += 1  # while i < len(shape)
        j += 1  # while j < len(input)

    helper = LayerHelper('broadcast_tensors', **locals())
    i = 0
    out = []
    while i < num_inputs:
        out.append(
            helper.create_variable_for_type_inference(
                dtype=helper.input_dtype()
            )
        )
        i += 1

    inputs = {'X': input}
    helper.append_op(
        type='broadcast_tensors', inputs=inputs, outputs={'Out': out}, attrs={}
    )

    return out


def flip(x, axis, name=None):
    """
    Reverse the order of a n-D tensor along given axis in axis.

    Args:
        x (Tensor): A Tensor(or LoDTensor) with shape :math:`[N_1, N_2,..., N_k]` . The data type of the input Tensor x
            should be float32, float64, int32, int64, bool.
        axis (list|tuple|int): The axis(axes) to flip on. Negative indices for indexing from the end are accepted.
        name (str, optional): Name for the operation (optional, default is None). For more information, please refer to :ref:`api_guide_Name`.

    Returns:
        Tensor, Tensor or LoDTensor calculated by flip layer. The data type is same with input x.

    Examples:
        .. code-block:: python

          import paddle

          image_shape=(3, 2, 2)
          img = paddle.arange(image_shape[0] * image_shape[1] * image_shape[2]).reshape(image_shape)
          tmp = paddle.flip(img, [0,1])
          print(tmp) # [[[10,11],[8, 9]], [[6, 7],[4, 5]], [[2, 3],[0, 1]]]

          out = paddle.flip(tmp,-1)
          print(out) # [[[11,10],[9, 8]], [[7, 6],[5, 4]], [[3, 2],[1, 0]]]
    """
    if isinstance(axis, int):
        axis = [axis]

    if in_dygraph_mode():
        return _C_ops.flip(x, axis)

    if paddle.in_dynamic_mode():
        return _legacy_C_ops.flip(x, "axis", axis)

    helper = LayerHelper("flip", **locals())
    check_type(x, 'X', (Variable), 'flip')
    dtype = helper.input_dtype('x')
    check_dtype(
        dtype,
        'X',
        ['float16', 'float32', 'float64', 'int32', 'int64', 'bool'],
        'flip',
    )
    check_type(axis, 'axis', (list, tuple), 'flip')
    if name is None:
        out = helper.create_variable_for_type_inference(dtype)
    else:
        out = helper.create_variable(name=name, dtype=dtype, persistable=False)

    helper.append_op(
        type="flip", inputs={"X": x}, outputs={"Out": out}, attrs={"axis": axis}
    )
    return out


def rot90(x, k=1, axes=[0, 1], name=None):
    """
    Rotate a n-D tensor by 90 degrees. The rotation direction and times are specified by axes and the absolute value of k. Rotation direction is from axes[0] towards axes[1] if k > 0, and from axes[1] towards axes[0] for k < 0.

    Args:
        x (Tensor): The input Tensor(or LoDTensor). The data type of the input Tensor x
            should be float16, float32, float64, int32, int64, bool. float16 is only supported on gpu.
        k (int, optional): Direction and number of times to rotate, default value: 1.
        axes (list|tuple, optional): Axes to rotate, dimension must be 2. default value: [0, 1].
        name (str, optional): The default value is None.  Normally there is no need for user to set this property.
            For more information, please refer to :ref:`api_guide_Name` .

    Returns:
        Tensor, Tensor or LoDTensor calculated by rot90 layer. The data type is same with input x.

    Examples:
        .. code-block:: python

          import paddle

          data = paddle.arange(4)
          data = paddle.reshape(data, (2, 2))
          print(data)
          #[[0, 1],
          # [2, 3]]

          y = paddle.rot90(data, 1, [0, 1])
          print(y)
          #[[1, 3],
          # [0, 2]]

          y= paddle.rot90(data, -1, [0, 1])
          print(y)
          #[[2, 0],
          # [3, 1]]

          data2 = paddle.arange(8)
          data2 = paddle.reshape(data2, (2,2,2))
          print(data2)
          #[[[0, 1],
          #  [2, 3]],
          # [[4, 5],
          #  [6, 7]]]

          y = paddle.rot90(data2, 1, [1, 2])
          print(y)
          #[[[1, 3],
          #  [0, 2]],
          # [[5, 7],
          #  [4, 6]]]
    """

    helper = LayerHelper("rot90", **locals())
    check_type(x, 'X', (Variable), 'rot90')
    dtype = helper.input_dtype('x')
    check_dtype(
        dtype,
        'X',
        ['float16', 'float32', 'float64', 'int32', 'int64', 'bool'],
        'rot90',
    )
    check_type(axes, 'axes', (list, tuple), 'rot90')

    input_total_dims = len(x.shape)
    total_rot_dims = len(axes)
    if total_rot_dims != 2:
        raise ValueError(
            "expected total rotation axes == 2, but got axes = {}".format(
                total_rot_dims
            )
        )
    if input_total_dims < 2:
        raise ValueError(
            "expected total dims >= 2, but got total dims = {}".format(
                input_total_dims
            )
        )

    if not (axes[0] != axes[1] and abs(axes[0] - axes[1]) != input_total_dims):
        raise ValueError(
            "expected rotation axes to be different, but got axis0 = {}, and axis1 = {}".format(
                axes[0], axes[1]
            )
        )

    if not (axes[0] < input_total_dims and axes[0] >= -input_total_dims):
        raise ValueError(
            "Rotation axis0 out of range, axis0 = {}".format(axes[0])
        )
    if not (axes[1] < input_total_dims and axes[1] >= -input_total_dims):
        raise ValueError(
            "Rotation axis1 out of range, axis1 = {}".format(axes[1])
        )

    k %= 4
    if k == 0:
        return x
    if k == 2:
        return flip(flip(x, axes[0]), axes[1])

    axes_list = list(range(0, input_total_dims))
    (axes_list[axes[0]], axes_list[axes[1]]) = (
        axes_list[axes[1]],
        axes_list[axes[0]],
    )
    if k == 1:
        return transpose(flip(x, axes[1]), axes_list)
    else:
        # k == 3
        return flip(transpose(x, axes_list), axes[1])


def flatten(x, start_axis=0, stop_axis=-1, name=None):
    r"""
    Flattens a contiguous range of axes in a tensor according to start_axis and stop_axis.

    Note:
        The output Tensor will share data with origin Tensor and doesn't have a Tensor copy in ``dygraph`` mode.
        If you want to use the Tensor copy version, please use `Tensor.clone` like ``flatten_clone_x = x.flatten().clone()``.

    For Example:

    .. code-block:: text

        Case 1:

          Given
            X.shape = (3, 100, 100, 4)

          and
            start_axis = 1
            end_axis = 2

          We get:
            Out.shape = (3, 1000 * 100, 2)

        Case 2:

          Given
            X.shape = (3, 100, 100, 4)

          and
            start_axis = 0
            stop_axis = -1

          We get:
            Out.shape = (3 * 100 * 100 * 4)

    Args:
        x (Tensor): A tensor of number of dimentions >= axis. A tensor with data type float32,
                      float64, int8, int32, int64, uint8.
        start_axis (int): the start axis to flatten
        stop_axis (int): the stop axis to flatten
        name (str, optional): Name for the operation (optional, default is None). For more information, please refer to :ref:`api_guide_Name`.

    Returns:
        Tensor, A tensor with the contents of the input tensor, with input \
                  axes flattened by indicated start axis and end axis. \
                  A Tensor with data type same as input x.

    Examples:

        .. code-block:: python

            import paddle

            image_shape=(2, 3, 4, 4)

            x = paddle.arange(end=image_shape[0] * image_shape[1] * image_shape[2] * image_shape[3])
            img = paddle.reshape(x, image_shape)

            out = paddle.flatten(img, start_axis=1, stop_axis=2)
            # out shape is [2, 12, 4]

            # out shares data with img in dygraph mode
            img[0, 0, 0, 0] = -1
            print(out[0, 0, 0]) # [-1]
    """
    if not (isinstance(x, Variable)):
        raise ValueError("The input x should be a Tensor")

    if not paddle.in_dynamic_mode():
        check_variable_and_dtype(
            x,
            'x',
            ['float32', 'float64', 'int8', 'int16', 'int32', 'int64', 'uint8'],
            'flatten',
        )

    x_dim = len(x.shape)
    if (
        not (isinstance(start_axis, int))
        or (start_axis > x_dim - 1)
        or start_axis < -x_dim
    ):
        raise ValueError(
            "The start_axis should be a int, and in range [-rank(x), rank(x))"
        )
    if (
        not (isinstance(stop_axis, int))
        or (stop_axis > x_dim - 1)
        or stop_axis < -x_dim
    ):
        raise ValueError(
            "The stop_axis should be a int, and in range [-rank(x), rank(x))"
        )
    if start_axis < 0:
        start_axis = start_axis + x_dim
    if stop_axis < 0:
        stop_axis = stop_axis + x_dim
    if start_axis > stop_axis:
        raise ValueError("The stop_axis should be larger than stat_axis")

    if in_dygraph_mode():
        return _C_ops.flatten(x, start_axis, stop_axis)

    if _in_legacy_dygraph():
        dy_out, _ = _legacy_C_ops.flatten_contiguous_range(
            x, 'start_axis', start_axis, 'stop_axis', stop_axis
        )
        return dy_out

    helper = LayerHelper('flatten', **locals())
    out = helper.create_variable_for_type_inference(x.dtype)
    x_shape = helper.create_variable_for_type_inference(x.dtype)
    helper.append_op(
        type='flatten_contiguous_range',
        inputs={"X": x},
        outputs={'Out': out, 'XShape': x_shape},
        attrs={"start_axis": start_axis, "stop_axis": stop_axis},
    )
    return out


@inplace_apis_in_dygraph_only
def flatten_(x, start_axis=0, stop_axis=-1, name=None):
    """
    Inplace version of ``flatten`` API, the output Tensor will be inplaced with input ``x``.
    Please refer to :ref:`api_tensor_flatten`.
    """
    if not (isinstance(x, Variable)):
        raise ValueError("The input x should be a Tensor")

    x_dim = len(x.shape)
    if (
        not (isinstance(start_axis, int))
        or (start_axis > x_dim - 1)
        or start_axis < -x_dim
    ):
        raise ValueError(
            "The start_axis should be a int, and in range [-rank(x), rank(x))"
        )
    if (
        not (isinstance(stop_axis, int))
        or (stop_axis > x_dim - 1)
        or stop_axis < -x_dim
    ):
        raise ValueError(
            "The stop_axis should be a int, and in range [-rank(x), rank(x))"
        )
    if start_axis < 0:
        start_axis = start_axis + x_dim
    if stop_axis < 0:
        stop_axis = stop_axis + x_dim
    if start_axis > stop_axis:
        raise ValueError("The stop_axis should be larger than stat_axis")

    if in_dygraph_mode():
        return _C_ops.flatten_(x, start_axis, stop_axis)

    if _in_legacy_dygraph():
        dy_out, _ = _legacy_C_ops.flatten_contiguous_range_(
            x, 'start_axis', start_axis, 'stop_axis', stop_axis
        )
        return dy_out


def roll(x, shifts, axis=None, name=None):
    """
    Roll the `x` tensor along the given axis(axes). With specific 'shifts', Elements that
    roll beyond the last position are re-introduced at the first according to 'shifts'.
    If a axis is not specified,
    the tensor will be flattened before rolling and then restored to the original shape.

    Args:
        x (Tensor): The x tensor as input.
        shifts (int|list|tuple): The number of places by which the elements
                           of the `x` tensor are shifted.
        axis (int|list|tuple, optional): axis(axes) along which to roll. Default: None
        name(str, optional): The default value is None.  Normally there is no need for user to set this property.
                For more information, please refer to :ref:`api_guide_Name` .


    Returns:
        Tensor, A Tensor with same data type as `x`.

    Examples:
        .. code-block:: python

            import paddle

            x = paddle.to_tensor([[1.0, 2.0, 3.0],
                                  [4.0, 5.0, 6.0],
                                  [7.0, 8.0, 9.0]])
            out_z1 = paddle.roll(x, shifts=1)
            print(out_z1)
            #[[9. 1. 2.]
            # [3. 4. 5.]
            # [6. 7. 8.]]
            out_z2 = paddle.roll(x, shifts=1, axis=0)
            print(out_z2)
            #[[7. 8. 9.]
            # [1. 2. 3.]
            # [4. 5. 6.]]
            out_z3 = paddle.roll(x, shifts=1, axis=1)
            print(out_z3)
            #[[3. 1. 2.]
            # [6. 4. 5.]
            # [9. 7. 8.]]
    """
    origin_shape = x.shape
    if type(shifts) == int:
        shifts = [shifts]
    if type(axis) == int:
        axis = [axis]

    len_origin_shape = len(origin_shape)
    if axis is not None:
        for i in range(len(axis)):
            if axis[i] >= len_origin_shape or axis[i] < -len_origin_shape:
                raise ValueError(
                    "axis is out of range, it should be in range [{}, {}), but received {}".format(
                        -len_origin_shape, len_origin_shape, axis
                    )
                )
    else:
        axis = []

    if in_dygraph_mode():
        return _C_ops.roll(x, shifts, axis)

    if _in_legacy_dygraph():
        return _legacy_C_ops.roll(x, 'axis', axis, 'shifts', shifts)

    helper = LayerHelper("roll", **locals())
    check_type(axis, 'axis', (list, tuple), 'roll')

    out = helper.create_variable_for_type_inference(x.dtype)

    if isinstance(shifts, Variable):
        helper.append_op(
            type='roll',
            inputs={'X': x, "ShiftsTensor": shifts},
            outputs={'Out': out},
            attrs={'axis': axis},
        )
    else:
        check_type(shifts, 'shifts', (list, tuple), 'roll')
        helper.append_op(
            type='roll',
            inputs={'X': x},
            outputs={'Out': out},
            attrs={'axis': axis, 'shifts': shifts},
        )
    return out


def stack(x, axis=0, name=None):
    """
    Stacks all the input tensors ``x`` along ``axis`` dimemsion.
    All tensors must be of the same shape and same dtype.

    For example, given N tensors of shape [A, B], if ``axis == 0``, the shape of stacked
    tensor is [N, A, B]; if ``axis == 1``, the shape of stacked
    tensor is [A, N, B], etc.


    .. code-block:: text

        Case 1:

          Input:
            x[0].shape = [1, 2]
            x[0].data = [ [1.0 , 2.0 ] ]
            x[1].shape = [1, 2]
            x[1].data = [ [3.0 , 4.0 ] ]
            x[2].shape = [1, 2]
            x[2].data = [ [5.0 , 6.0 ] ]

          Attrs:
            axis = 0

          Output:
            Out.dims = [3, 1, 2]
            Out.data =[ [ [1.0, 2.0] ],
                        [ [3.0, 4.0] ],
                        [ [5.0, 6.0] ] ]


        Case 2:

          Input:
            x[0].shape = [1, 2]
            x[0].data = [ [1.0 , 2.0 ] ]
            x[1].shape = [1, 2]
            x[1].data = [ [3.0 , 4.0 ] ]
            x[2].shape = [1, 2]
            x[2].data = [ [5.0 , 6.0 ] ]


          Attrs:
            axis = 1 or axis = -2  # If axis = -2, axis = axis+ndim(x[0])+1 = -2+2+1 = 1.

          Output:
            Out.shape = [1, 3, 2]
            Out.data =[ [ [1.0, 2.0]
                          [3.0, 4.0]
                          [5.0, 6.0] ] ]

    Args:
        x (list[Tensor]|tuple[Tensor]): Input ``x`` can be a ``list`` or ``tuple`` of tensors, the Tensors in ``x``
                                     must be of the same shape and dtype. Supported data types: float32, float64, int32, int64.
        axis (int, optional): The axis along which all inputs are stacked. ``axis`` range is ``[-(R+1), R+1)``,
                              where ``R`` is the number of dimensions of the first input tensor ``x[0]``.
                              If ``axis < 0``, ``axis = axis+R+1``. The default value of axis is 0.
        name (str, optional): Name for the operation (optional, default is None). For more information, please refer to :ref:`api_guide_Name`.

    Returns:
        Tensor, The stacked tensor with same data type as input.

    Example:
        .. code-block:: python

            import paddle

            x1 = paddle.to_tensor([[1.0, 2.0]])
            x2 = paddle.to_tensor([[3.0, 4.0]])
            x3 = paddle.to_tensor([[5.0, 6.0]])

            out = paddle.stack([x1, x2, x3], axis=0)
            print(out.shape)  # [3, 1, 2]
            print(out)
            # [[[1., 2.]],
            #  [[3., 4.]],
            #  [[5., 6.]]]

        out = paddle.stack([x1, x2, x3], axis=-2)
        print(out.shape)  # [1, 3, 2]
        print(out)
        # [[[1., 2.],
        #   [3., 4.],
        #   [5., 6.]]]
    """
    axis = 0 if axis is None else axis

    if in_dygraph_mode():
        return _C_ops.stack(x, axis)

    if _in_legacy_dygraph():
        return _legacy_C_ops.stack(x, 'axis', axis)

    if not isinstance(x, list) and not isinstance(x, tuple):
        # NOTE:(zhiqiu) Only support Variable as input if the Variable is a LOD_TENSOR_ARRAY create by create_array, array_write, array_read, etc.
        # In that case, Variable is array of tensors indeed.
        if (
            isinstance(x, Variable)
            and x.desc.type() == core.VarDesc.VarType.LOD_TENSOR_ARRAY
        ):
            x = [x]
        else:
            raise TypeError(
                "The type of '%s' in %s must be %s, but received %s"
                % (
                    'x',
                    'stack',
                    'list[Tensor], tuple[Tensor] or TensorArray',
                    type(x),
                )
            )

    helper = LayerHelper('stack', **locals())

    out = helper.create_variable_for_type_inference(x[0].dtype)
    if x[0].desc.type() == core.VarDesc.VarType.LOD_TENSOR_ARRAY:
        assert len(x) == 1, (
            "If the elements of 'x' in stack are Variable(LoDTensorArray), "
            "number of the elements must be 1, but received %s." % len(x)
        )
        out_index = helper.create_variable_for_type_inference(dtype="int32")

        for i in x:
            check_variable_and_dtype(
                i,
                'x',
                ['float16', 'float32', 'float64', 'int32', 'int64'],
                'stack',
            )

        helper.append_op(
            type='tensor_array_to_tensor',
            inputs={'X': x[0]},
            outputs={'Out': [out], 'OutIndex': [out_index]},
            attrs={'axis': axis, 'use_stack': True},
        )
    else:
        helper.append_op(
            type='stack',
            inputs={'X': x},
            outputs={'Y': out},
            attrs={'axis': axis},
        )

    return out


def split(x, num_or_sections, axis=0, name=None):
    """
    Split the input tensor into multiple sub-Tensors.

    Args:
        x (Tensor): A N-D Tensor. The data type is bool, float16, float32, float64, uint8, int8, int32 or int64.
        num_or_sections (int|list|tuple): If ``num_or_sections`` is an int, then ``num_or_sections``
            indicates the number of equal sized sub-Tensors that the ``x`` will be divided into.
            If ``num_or_sections`` is a list or tuple, the length of it indicates the number of
            sub-Tensors and the elements in it indicate the sizes of sub-Tensors'  dimension orderly.
            The length of the list must not  be larger than the ``x`` 's size of specified ``axis``.
        axis (int|Tensor, optional): The axis along which to split, it can be a scalar with type
            ``int`` or a ``Tensor`` with shape [1] and data type  ``int32`` or ``int64``.
            If :math::`axis < 0`, the axis to split along is :math:`rank(x) + axis`. Default is 0.
        name (str, optional): The default value is None.  Normally there is no need for user to set this property.
            For more information, please refer to :ref:`api_guide_Name` .
    Returns:
        list(Tensor), The list of segmented Tensors.

    Example:
        .. code-block:: python

            import paddle

            # x is a Tensor of shape [3, 9, 5]
            x = paddle.rand([3, 9, 5])

            out0, out1, out2 = paddle.split(x, num_or_sections=3, axis=1)
            print(out0.shape)  # [3, 3, 5]
            print(out1.shape)  # [3, 3, 5]
            print(out2.shape)  # [3, 3, 5]

            out0, out1, out2 = paddle.split(x, num_or_sections=[2, 3, 4], axis=1)
            print(out0.shape)  # [3, 2, 5]
            print(out1.shape)  # [3, 3, 5]
            print(out2.shape)  # [3, 4, 5]

            out0, out1, out2 = paddle.split(x, num_or_sections=[2, 3, -1], axis=1)
            print(out0.shape)  # [3, 2, 5]
            print(out1.shape)  # [3, 3, 5]
            print(out2.shape)  # [3, 4, 5]

            # axis is negative, the real axis is (rank(x) + axis)=1
            out0, out1, out2 = paddle.split(x, num_or_sections=3, axis=-2)
            print(out0.shape)  # [3, 3, 5]
            print(out1.shape)  # [3, 3, 5]
            print(out2.shape)  # [3, 3, 5]
    """
    input = x
    dim = axis
    if _non_static_mode():
        num = None
        attrs = ()

        if isinstance(dim, Variable):
            dim = dim.numpy()
            dim = dim.item(0)
        assert len(input.shape) + dim >= 0, "(rank(x) + axis) must >= 0"
        dim = (len(input.shape) + dim) if dim < 0 else dim
        attrs += ('axis', dim)

        if isinstance(num_or_sections, int):
            num = num_or_sections
            attrs += ('num', num_or_sections)
        elif isinstance(num_or_sections, (list, tuple)):
            num = len(num_or_sections)
            if utils._contain_var(num_or_sections):
                for index, item in enumerate(num_or_sections):
                    if isinstance(item, Variable):
                        num_or_sections[index] = num_or_sections[index].numpy()[
                            0
                        ]
                attrs += ('sections', list(num_or_sections))
            else:
                attrs += ('sections', list(num_or_sections))
        else:
            raise TypeError(
                "The type of 'num_or_sections' in split must be int, list or tuple in imperative mode, but "
                "received %s." % (type(num_or_sections))
            )
        if in_dygraph_mode():
            if isinstance(num_or_sections, int):
                return _C_ops.split_with_num(input, num_or_sections, dim)
            else:
                return _C_ops.split(input, num_or_sections, dim)
        elif _in_legacy_dygraph():
            out = [_varbase_creator() for n in range(num)]
            _legacy_C_ops.split(input, out, *attrs)
            return out

    check_variable_and_dtype(
        input,
        'input',
        [
            'bool',
            'float16',
            'float32',
            'float64',
            'int32',
            'int64',
            'uint8',
            'int8',
        ],
        'split',
    )
    check_type(num_or_sections, 'num_or_sections', (list, int, tuple), 'split')
    check_type(dim, 'dim', (int, Variable), 'split')
    if isinstance(dim, Variable):
        check_dtype(dim.dtype, 'dim', ['int32', 'int64'], 'split')

    helper = LayerHelper('split', **locals())

    input_shape = input.shape
    inputs = {'X': input}
    attrs = {'num': num_or_sections if isinstance(num_or_sections, int) else 0}

    def _get_SectionsTensorList(one_list):
        tensor_list = []
        unk_dim_idx = -1
        for idx, dim_size in enumerate(one_list):
            if isinstance(dim_size, Variable):
                dim_size.stop_gradient = True
                tensor_list.append(dim_size)
            else:
                assert isinstance(dim_size, int)
                if dim_size == -1:
                    assert unk_dim_idx == -1, (
                        "Only one value of 'num_or_section' in split can "
                        "be -1. But received num_or_section[%d] is also -1."
                        % idx
                    )
                    unk_dim_idx = idx
                temp_out = helper.create_variable_for_type_inference('int32')
                fill_constant(
                    [1], 'int32', dim_size, force_cpu=True, out=temp_out
                )
                tensor_list.append(temp_out)
        return tensor_list

    if isinstance(dim, Variable):
        dim.stop_gradient = True
        inputs['AxisTensor'] = dim
    else:
        assert len(input.shape) + dim >= 0, "(rank(x) + axis) must >= 0"
        dim = (len(input_shape) + dim) if dim < 0 else dim
        attrs['axis'] = dim

    if isinstance(num_or_sections, int):
        assert num_or_sections > 1, 'num_or_sections must be more than 1.'
        if isinstance(dim, int) and input_shape[dim] > 0:
            assert input_shape[dim] % num_or_sections == 0, (
                "The input's size along the split dimension "
                "must be evenly divisible by Attr(num_or_sections). "
                "But %d is not evenly divisible by %d. "
                % (num_or_sections, input_shape[dim])
            )
        num = num_or_sections
    else:
        if isinstance(dim, int) and input_shape[dim] > 0:
            assert (
                len(num_or_sections) <= input_shape[dim]
            ), 'len(num_or_sections) must not be more than input.shape[dim].'
        num = len(num_or_sections)
        attrs['sections'] = list(
            map(
                lambda ele: -1 if isinstance(ele, Variable) else ele,
                num_or_sections,
            )
        )
        if utils._contain_var(num_or_sections):
            inputs['SectionsTensorList'] = _get_SectionsTensorList(
                num_or_sections
            )

    outs = [
        helper.create_variable_for_type_inference(dtype=helper.input_dtype())
        for i in range(num)
    ]
    helper.append_op(
        type='split', inputs=inputs, outputs={'Out': outs}, attrs=attrs
    )
    return outs


def vsplit(x, num_or_sections, name=None):
    """
    Split the input tensor into multiple sub-Tensors along the vertical axis, which is equivalent to ``paddle.split`` with ``axis=0``.

    Args:
        x (Tensor): A Tensor whose dimension must be greater than 1. The data type is bool, float16, float32, float64, uint8, int8, int32 or int64.
        num_or_sections (int|list|tuple): If ``num_or_sections`` is an int, then ``num_or_sections``
            indicates the number of equal sized sub-Tensors that the ``x`` will be divided into.
            If ``num_or_sections`` is a list or tuple, the length of it indicates the number of
            sub-Tensors and the elements in it indicate the sizes of sub-Tensors'  dimension orderly.
            The length of the list must not  be larger than the ``x`` 's size of axis 0.
        name (str, optional): The default value is None.  Normally there is no need for user to set this property.
            For more information, please refer to :ref:`api_guide_Name` .
    Returns:
        list[Tensor], The list of segmented Tensors.

    Example:
        .. code-block:: python

            import paddle

            # x is a Tensor of shape [8, 6, 7]
            x = paddle.rand([8, 6, 7])
            out0, out1, out2 = paddle.vsplit(x, num_or_sections=2)
            print(out0.shape)  # [4, 6, 7]
            print(out1.shape)  # [4, 6, 7]
            out0, out1, out2 = paddle.vsplit(x, num_or_sections=[1, 3, 4])
            print(out0.shape)  # [1, 6, 7]
            print(out1.shape)  # [3, 6, 7]
            print(out2.shape)  # [4, 6, 7]
            out0, out1, out2 = paddle.vsplit(x, num_or_sections=[2, 3, -1])
            print(out0.shape)  # [2, 6, 7]
            print(out1.shape)  # [3, 6, 7]
            print(out2.shape)  # [3, 6, 7]
    """
    if x.ndim < 2:
        raise ValueError(
            "The input tensor's dimension must be greater than 1, but got {}".format(
                x.ndim
            )
        )
    return split(x, num_or_sections, axis=0, name=name)


def squeeze(x, axis=None, name=None):
    """
    Squeeze the dimension(s) of size 1 of input tensor x's shape.

    Note that the output Tensor will share data with origin Tensor and doesn't have a
    Tensor copy in ``dygraph`` mode. If you want to use the Tensor copy version,
    please use `Tensor.clone` like ``squeeze_clone_x = x.squeeze().clone()``.

    If axis is provided, it will remove the dimension(s) by given axis that of size 1.
    If the dimension of given axis is not of size 1, the dimension remain unchanged.
    If axis is not provided, all dims equal of size 1 will be removed.

    .. code-block:: text

        Case1:

          Input:
            x.shape = [1, 3, 1, 5]  # If axis is not provided, all dims equal of size 1 will be removed.
            axis = None
          Output:
            out.shape = [3, 5]

        Case2:

          Input:
            x.shape = [1, 3, 1, 5]  # If axis is provided, it will remove the dimension(s) by given axis that of size 1.
            axis = 0
          Output:
            out.shape = [3, 1, 5]

        Case4:

          Input:
            x.shape = [1, 3, 1, 5]  # If the dimension of one given axis (3) is not of size 1, the dimension remain unchanged.
            axis = [0, 2, 3]
          Output:
            out.shape = [3, 5]

        Case4:

          Input:
            x.shape = [1, 3, 1, 5]  # If axis is negative, axis = axis + ndim (number of dimensions in x).
            axis = [-2]
          Output:
            out.shape = [1, 3, 5]

    Args:
        x (Tensor): The input Tensor. Supported data type: float32, float64, bool, int8, int32, int64.
        axis (int|list|tuple, optional): An integer or list/tuple of integers, indicating the dimensions to be squeezed. Default is None.
                          The range of axis is :math:`[-ndim(x), ndim(x))`.
                          If axis is negative, :math:`axis = axis + ndim(x)`.
                          If axis is None, all the dimensions of x of size 1 will be removed.
        name (str, optional): Please refer to :ref:`api_guide_Name`, Default None.

    Returns:
        Tensor, Squeezed Tensor with the same data type as input Tensor.

    Examples:
        .. code-block:: python

            import paddle

            x = paddle.rand([5, 1, 10])
            output = paddle.squeeze(x, axis=1)

            print(x.shape)  # [5, 1, 10]
            print(output.shape)  # [5, 10]

            # output shares data with x in dygraph mode
            x[0, 0, 0] = 10.
            print(output[0, 0]) # [10.]

    """
    if axis is None:
        axis = []
    elif isinstance(axis, int):
        axis = [axis]
    elif isinstance(axis, tuple):
        axis = list(axis)

    input = x
    axes = axis
    if in_dygraph_mode():
        return _C_ops.squeeze(input, axes)
    if _in_legacy_dygraph():
        out, _ = _legacy_C_ops.squeeze2(input, 'axes', axes)
        return out

    helper = LayerHelper("squeeze", **locals())
    check_variable_and_dtype(
        input,
        'input',
        [
            'float16',
            'float32',
            'float64',
            'bool',
            'int8',
            'int32',
            'int64',
            'complex64',
            'complex128',
        ],
        'squeeze',
    )

    check_type(axes, 'axis/axes', (int, list, tuple, Variable), 'squeeze')
    attrs = {}
    if isinstance(axes, Variable):
        axes.stop_gradient = True
        attrs["axes"] = axes
    elif isinstance(axes, (list, tuple)):
        if utils._contain_var(axes):
            attrs["axes"] = utils._convert_to_tensor_list(axes)
        else:
            attrs["axes"] = axes

    out = helper.create_variable_for_type_inference(dtype=input.dtype)
    x_shape = helper.create_variable_for_type_inference(dtype=input.dtype)
    helper.append_op(
        type="squeeze2",
        inputs={"X": input},
        attrs=attrs,
        outputs={"Out": out, "XShape": x_shape},
    )

    return out


@inplace_apis_in_dygraph_only
def squeeze_(x, axis=None, name=None):
    """
    Inplace version of ``squeeze`` API, the output Tensor will be inplaced with input ``x``.
    Please refer to :ref:`api_paddle_tensor_squeeze`.
    """
    if axis is None:
        axis = []
    elif isinstance(axis, int):
        axis = [axis]
    elif isinstance(axis, tuple):
        axis = list(axis)

    input = x
    axes = axis
    if in_dygraph_mode():
        return _C_ops.squeeze_(input, axes)
    if _in_legacy_dygraph():
        out, _ = _legacy_C_ops.squeeze2_(input, 'axes', axes)
        return out


def unique_consecutive(
    x,
    return_inverse=False,
    return_counts=False,
    axis=None,
    dtype="int64",
    name=None,
):
    """
    Eliminates all but the first element from every consecutive group of equivalent elements.

    Note:
        This function is different from :ref:`api_paddle_unique` in the sense that this function
        only eliminates consecutive duplicate values. This semantics is similar to :ref:`api_paddle_unique` in C++.

    Args:
        x(Tensor): the input tensor, it's data type should be float32, float64, int32, int64.
        return_inverse(bool, optional): If True, also return the indices for where elements in
            the original input ended up in the returned unique consecutive tensor. Default is False.
        return_counts(bool, optional): If True, also return the counts for each unique consecutive element.
            Default is False.
        axis(int, optional): The axis to apply unique consecutive. If None, the input will be flattened.
            Default is None.
        dtype(np.dtype|str, optional): The data type `inverse` tensor: int32 or int64.
            Default: int64.
        name(str, optional): Name for the operation. For more information, please refer to
            :ref:`api_guide_Name`. Default is None.

    Returns:
        - out (Tensor), the unique consecutive tensor for x.
        - inverse (Tensor), the element of the input tensor corresponds to
            the index of the elements in the unique consecutive tensor for x.
            inverse is provided only if return_inverse is True.
        - counts (Tensor), the counts of the every unique consecutive element in the input tensor.
            counts is provided only if return_counts is True.

    Example:
        .. code-block:: python

            import paddle

            x = paddle.to_tensor([1, 1, 2, 2, 3, 1, 1, 2])
            output = paddle.unique_consecutive(x) #
            print(output)
            # Tensor(shape=[5], dtype=int64, place=Place(gpu:0), stop_gradient=True,
            #        [1, 2, 3, 1, 2])

            _, inverse, counts = paddle.unique_consecutive(x, return_inverse=True, return_counts=True)
            print(inverse)
            # Tensor(shape=[8], dtype=int64, place=Place(gpu:0), stop_gradient=True,
            #        [0, 0, 1, 1, 2, 3, 3, 4])
            print(counts)
            # Tensor(shape=[5], dtype=int64, place=Place(gpu:0), stop_gradient=True,
            #        [2, 2, 1, 2, 1])

            x = paddle.to_tensor([[2, 1, 3], [3, 0, 1], [2, 1, 3], [2, 1, 3]])
            output = paddle.unique_consecutive(x, axis=0) #
            print(output)
            # Tensor(shape=[3, 3], dtype=int64, place=Place(gpu:0), stop_gradient=True,
            #        [[2, 1, 3],
            #         [3, 0, 1],
            #         [2, 1, 3]])

            x = paddle.to_tensor([[2, 1, 3], [3, 0, 1], [2, 1, 3], [2, 1, 3]])
            output = paddle.unique_consecutive(x, axis=0) #
            print(output)
            # Tensor(shape=[3, 3], dtype=int64, place=Place(gpu:0), stop_gradient=True,
            #        [[2, 1, 3],
            #         [3, 0, 1],
            #         [2, 1, 3]])
    """

    if axis is None:
        axis = []
    else:
        axis = [axis]
    attr_dtype = convert_np_dtype_to_dtype_(dtype)
    if in_dygraph_mode():
        out, inverse, counts = _C_ops.unique_consecutive(
            x, return_inverse, return_counts, axis, attr_dtype
        )
        outs = [out]
        if return_inverse:
            outs.append(inverse)
        if return_counts:
            outs.append(counts)
        if len(outs) == 1:
            return outs[0]
        return tuple(outs)
    elif paddle.in_dynamic_mode():
        out, inverse, counts = _legacy_C_ops.unique_consecutive(
            x,
            'dtype',
            attr_dtype,
            'return_inverse',
            return_inverse,
            'return_counts',
            return_counts,
            'axis',
            axis,
        )
        outs = [out]
        if return_inverse:
            outs.append(inverse)
        if return_counts:
            outs.append(counts)
        if len(outs) == 1:
            return outs[0]
        return tuple(outs)
    check_variable_and_dtype(
        x,
        "input",
        ['float32', 'float64', 'int32', 'int64'],
        'unique_consecutive',
    )
    check_type(return_inverse, 'return_inverse', bool, 'unique_consecutive')
    check_type(return_counts, 'return_counts', bool, 'unique_consecutive')
    check_dtype(dtype, 'dtype', ['int32', 'int64'], 'unique_consecutive')
    if len(axis) != 0:
        check_type(axis[0], 'axis', int, 'unique_consecutive')
    helper = LayerHelper('unique_consecutive', **locals())
    attrs = {
        'dtype': attr_dtype,
        "return_inverse": return_inverse,
        "return_counts": return_counts,
        "axis": axis,
    }
    out = helper.create_variable_for_type_inference(
        dtype=x.dtype, stop_gradient=True
    )
    inverse = helper.create_variable_for_type_inference(
        dtype=attr_dtype, stop_gradient=True
    )
    counts = helper.create_variable_for_type_inference(
        dtype=attr_dtype, stop_gradient=True
    )
    outputs = {"Out": out, "Index": inverse, "Counts": counts}
    outs = [out]
    if return_inverse:
        outs.append(inverse)
    if return_counts:
        outs.append(counts)
    helper.append_op(
        type="unique_consecutive", inputs={"X": x}, attrs=attrs, outputs=outputs
    )
    if len(outs) == 1:
        return outs[0]
    return tuple(outs)


def unique(
    x,
    return_index=False,
    return_inverse=False,
    return_counts=False,
    axis=None,
    dtype="int64",
    name=None,
):
    r"""
    Returns the unique elements of `x` in ascending order.

    Args:
        x(Tensor): The input tensor, it's data type should be float32, float64, int32, int64.
        return_index(bool, optional): If True, also return the indices of the input tensor that
            result in the unique Tensor.
        return_inverse(bool, optional): If True, also return the indices for where elements in
            the original input ended up in the returned unique tensor.
        return_counts(bool, optional): If True, also return the counts for each unique element.
        axis(int, optional): The axis to apply unique. If None, the input will be flattened.
            Default: None.
        dtype(np.dtype|str, optional): The date type of `indices` or `inverse` tensor: int32 or int64.
            Default: int64.
        name(str, optional): Name for the operation. For more information, please refer to
            :ref:`api_guide_Name`. Default: None.

    Returns:
        tuple (out, indices, inverse, counts). `out` is the unique tensor for `x`. `indices` is \
            provided only if `return_index` is True. `inverse` is provided only if `return_inverse` \
            is True. `counts` is provided only if `return_counts` is True.

    Examples:
        .. code-block:: python

            import paddle

            x = paddle.to_tensor([2, 3, 3, 1, 5, 3])
            unique = paddle.unique(x)
            print(unique)
            # Tensor(shape=[4], dtype=int64, place=Place(gpu:0), stop_gradient=True,
            #        [1, 2, 3, 5])

            _, indices, inverse, counts = paddle.unique(x, return_index=True, return_inverse=True, return_counts=True)
            print(indices)
            # Tensor(shape=[4], dtype=int64, place=Place(gpu:0), stop_gradient=True,
            #        [3, 0, 1, 4])
            print(inverse)
            # Tensor(shape=[6], dtype=int64, place=Place(gpu:0), stop_gradient=True,
            #        [1, 2, 2, 0, 3, 2])
            print(counts)
            # Tensor(shape=[4], dtype=int64, place=Place(gpu:0), stop_gradient=True,
            #        [1, 1, 3, 1])

            x = paddle.to_tensor([[2, 1, 3], [3, 0, 1], [2, 1, 3]])
            unique = paddle.unique(x)
            print(unique)
            # Tensor(shape=[4], dtype=int64, place=Place(gpu:0), stop_gradient=True,
            #        [0, 1, 2, 3])

            unique = paddle.unique(x, axis=0)
            print(unique)
            # Tensor(shape=[2, 3], dtype=int64, place=Place(gpu:0), stop_gradient=True,
            #        [[2, 1, 3],
            #         [3, 0, 1]])
    """
    if axis is None:
        axis = []
    else:
        axis = [axis]
    attr_dtype = convert_np_dtype_to_dtype_(dtype)
    if _non_static_mode():
        if in_dygraph_mode():
            out, indices, inverse, counts = _C_ops.unique(
                x, return_index, return_inverse, return_counts, axis, attr_dtype
            )
        if _in_legacy_dygraph():
            out, inverse, indices, counts = _legacy_C_ops.unique(
                x,
                'dtype',
                attr_dtype,
                'return_index',
                return_index,
                'return_inverse',
                return_inverse,
                'return_counts',
                return_counts,
                'axis',
                axis,
                "is_sorted",
                True,
            )
        outs = [out]
        if return_index:
            outs.append(indices)
        if return_inverse:
            outs.append(inverse)
        if return_counts:
            outs.append(counts)

        if len(outs) == 1:
            return outs[0]

        return tuple(outs)

    check_variable_and_dtype(
        x, "input", ['float32', 'float64', 'int32', 'int64'], 'unique'
    )
    check_type(return_index, 'return_index', bool, 'unique')
    check_type(return_inverse, 'return_inverse', bool, 'unique')
    check_type(return_counts, 'return_counts', bool, 'unique')
    check_dtype(dtype, 'dtype', ['int32', 'int64'], 'unique')
    if len(axis) != 0:
        check_type(axis[0], 'axis', int, 'unique')

    helper = LayerHelper('unique', **locals())
    attrs = {
        'dtype': attr_dtype,
        "return_index": return_index,
        "return_inverse": return_inverse,
        "return_counts": return_counts,
        "axis": axis,
        "is_sorted": True,
    }
    out = helper.create_variable_for_type_inference(
        dtype=x.dtype, stop_gradient=True
    )
    indices = helper.create_variable_for_type_inference(
        dtype=attr_dtype, stop_gradient=True
    )
    inverse = helper.create_variable_for_type_inference(
        dtype=attr_dtype, stop_gradient=True
    )
    counts = helper.create_variable_for_type_inference(
        dtype=attr_dtype, stop_gradient=True
    )
    outputs = {
        "Out": out,
        "Indices": indices,
        "Index": inverse,
        "Counts": counts,
    }
    outs = [out]
    if return_index:
        outs.append(indices)
    if return_inverse:
        outs.append(inverse)
    if return_counts:
        outs.append(counts)

    helper.append_op(
        type="unique", inputs={"X": x}, attrs=attrs, outputs=outputs
    )

    if len(outs) == 1:
        return outs[0]

    return tuple(outs)


def unsqueeze(x, axis, name=None):
    """
    Insert single-dimensional entries to the shape of input Tensor ``x``. Takes one
    required argument axis, a dimension or list of dimensions that will be inserted.
    Dimension indices in axis are as seen in the output tensor.

    Note that the output Tensor will share data with origin Tensor and doesn't have a
    Tensor copy in ``dygraph`` mode. If you want to use the Tensor copy version,
    please use `Tensor.clone` like ``unsqueeze_clone_x = x.unsqueeze(-1).clone()``.

    Args:
        x (Tensor): The input Tensor to be unsqueezed. Supported data type: float32, float64, bool, int8, int32, int64.
        axis (int|list|tuple|Tensor): Indicates the dimensions to be inserted. The data type is ``int32`` .
                                    If ``axis`` is a list or tuple, the elements of it should be integers or Tensors with shape [1].
                                    If ``axis`` is a Tensor, it should be an 1-D Tensor .
                                    If ``axis`` is negative, ``axis = axis + ndim(x) + 1``.
        name (str|None): Name for this layer. Please refer to :ref:`api_guide_Name`, Default None.

    Returns:
        Tensor, Unsqueezed Tensor with the same data type as input Tensor.

    Examples:
        .. code-block:: python

            import paddle

            x = paddle.rand([5, 10])
            print(x.shape)  # [5, 10]

            out1 = paddle.unsqueeze(x, axis=0)
            print(out1.shape)  # [1, 5, 10]

            out2 = paddle.unsqueeze(x, axis=[0, 2])
            print(out2.shape)  # [1, 5, 1, 10]

            axis = paddle.to_tensor([0, 1, 2])
            out3 = paddle.unsqueeze(x, axis=axis)
            print(out3.shape)  # [1, 1, 1, 5, 10]

            # out1, out2, out3 share data with x in dygraph mode
            x[0, 0] = 10.
            print(out1[0, 0, 0]) # [10.]
            print(out2[0, 0, 0, 0]) # [10.]
            print(out3[0, 0, 0, 0, 0]) # [10.]

    """
    input = x
    axes = axis
    if _non_static_mode():
        if isinstance(axes, int):
            axes = [axes]
        elif isinstance(axes, Variable):
            axes = axes.numpy().tolist()
        elif isinstance(axes, (list, tuple)):
            axes = [
                item.numpy().item(0) if isinstance(item, Variable) else item
                for item in axes
            ]
        if _in_legacy_dygraph():
            out, _ = _legacy_C_ops.unsqueeze2(input, 'axes', axes)
            return out
        return _C_ops.unsqueeze(input, axes)

    check_type(axes, 'axis/axes', (int, list, tuple, Variable), 'unsqueeze')
    check_variable_and_dtype(
        input,
        'input',
        [
            'float16',
            'float32',
            'float64',
            'bool',
            'int8',
            'int16',
            'int32',
            'int64',
            'complex64',
            'complex128',
        ],
        'unsqueeze',
    )
    helper = LayerHelper("unsqueeze2", **locals())
    inputs = {"X": input}
    attrs = {}

    if isinstance(axes, int):
        axes = [axes]
    if isinstance(axes, Variable):
        axes.stop_gradient = True
        inputs["AxesTensor"] = axes
    elif isinstance(axes, (list, tuple)):
        if utils._contain_var(axes):
            inputs["AxesTensorList"] = utils._convert_to_tensor_list(axes)
        else:
            attrs["axes"] = axes

    out = helper.create_variable_for_type_inference(dtype=input.dtype)
    x_shape = helper.create_variable_for_type_inference(dtype=input.dtype)
    helper.append_op(
        type="unsqueeze2",
        inputs=inputs,
        attrs=attrs,
        outputs={"Out": out, "XShape": x_shape},
    )

    return out


@inplace_apis_in_dygraph_only
def unsqueeze_(x, axis, name=None):
    """
    Inplace version of ``unsqueeze`` API, the output Tensor will be inplaced with input ``x``.
    Please refer to :ref:`api_paddle_tensor_unsqueeze`.
    """
    input = x
    axes = axis
    if isinstance(axes, int):
        axes = [axes]
    elif isinstance(axes, Variable):
        axes = axes.numpy().tolist()
    elif isinstance(axes, (list, tuple)):
        axes = [
            item.numpy().item(0) if isinstance(item, Variable) else item
            for item in axes
        ]
    if in_dygraph_mode():
        return _C_ops.unsqueeze_(input, axes)
    out, _ = _legacy_C_ops.unsqueeze2_(input, 'axes', axes)
    return out


def gather(x, index, axis=None, name=None):
    """
    Output is obtained by gathering entries of ``axis``
    of ``x`` indexed by ``index`` and concatenate them together.

    .. code-block:: text


                Given:

                x = [[1, 2],
                     [3, 4],
                     [5, 6]]

                index = [1, 2]
                axis=[0]

                Then:

                out = [[3, 4],
                       [5, 6]]

    Args:
        x (Tensor): The source input tensor with rank>=1. Supported data type is
            int32, int64, float32, float64 and uint8 (only for CPU),
            float16 (only for GPU).
        index (Tensor): The index input tensor with rank=0 or rank=1. Data type is int32 or int64.
        axis (Tensor|int, optional): The axis of input to be gathered, it's can be int or a Tensor with data type is int32 or int64. The default value is None, if None, the ``axis`` is 0.
        name (str, optional): The default value is None.  Normally there is no need for user to set this property.
            For more information, please refer to :ref:`api_guide_Name` .

    Returns:
        output (Tensor), If the index is a 1-D tensor, the output is a tensor with the same shape as ``x``. If the index is a 0-D tensor, the output will reduce the dimension where the axis pointing.

    Examples:

        .. code-block:: python

            import paddle

            input = paddle.to_tensor([[1,2],[3,4],[5,6]])
            index = paddle.to_tensor([0,1])
            output = paddle.gather(input, index, axis=0)
            # expected output: [[1,2],[3,4]]
    """
    if axis is None:
        axis = 0

    if in_dygraph_mode():
        return _C_ops.gather(x, index, axis)
    if _in_legacy_dygraph():
        axis = axis.item() if isinstance(axis, paddle.Tensor) else axis
        return _legacy_C_ops.gather(
            x, index, None, "axis", axis, "overwrite", False
        )

    check_variable_and_dtype(
        x,
        'x',
        ['float16', 'float32', 'float64', 'int16', 'int32', 'int64', 'uint8'],
        'gather',
    )
    check_variable_and_dtype(index, 'index', ['int32', 'int64'], 'gather')

    if isinstance(axis, Variable):
        check_variable_and_dtype(axis, 'axis', ['int32', 'int64'], 'gather')

    helper = LayerHelper('gather', **locals())
    dtype = helper.input_dtype('x')
    out = helper.create_variable_for_type_inference(dtype)
    if not isinstance(axis, Variable):
        helper.append_op(
            type="gather",
            inputs={"X": x, "Index": index},
            attrs={'axis': axis, 'overwrite': False},
            outputs={"Out": out},
        )
    else:
        helper.append_op(
            type="gather",
            inputs={"X": x, "Index": index, "Axis": axis},
            attrs={"overwrite": False},
            outputs={"Out": out},
        )

    return out


def unbind(input, axis=0):
    """

    Removes a tensor dimension, then split the input tensor into multiple sub-Tensors.

    Args:
        input (Tensor): The input variable which is an N-D Tensor, data type being float32, float64, int32 or int64.
        axis (int32|int64, optional): A scalar with type ``int32|int64`` shape [1]. The dimension along which to unbind.
            If :math:`axis < 0`, the dimension to unbind along is :math:`rank(input) + axis`. Default is 0.
    Returns:
        list(Tensor), The list of segmented Tensor variables.

    Example:
        .. code-block:: python

            import paddle

            # input is a Tensor which shape is [3, 4, 5]
            input = paddle.rand([3, 4, 5])

            [x0, x1, x2] = paddle.unbind(input, axis=0)
            # x0.shape [4, 5]
            # x1.shape [4, 5]
            # x2.shape [4, 5]

            [x0, x1, x2, x3] = paddle.unbind(input, axis=1)
            # x0.shape [3, 5]
            # x1.shape [3, 5]
            # x2.shape [3, 5]
            # x3.shape [3, 5]
    """
    if in_dygraph_mode():
        return _C_ops.unbind(input, axis)

    if not isinstance(axis, (int)):
        raise TypeError(
            "The type of 'axis'  must be int, but received %s." % (type(axis))
        )
    if isinstance(axis, np.generic):
        axis = np.asscalar(axis)
    input_shape = input.shape
    axis_ = axis if axis >= 0 else len(input_shape) + axis
    num = input_shape[axis_]
    if _in_legacy_dygraph():
        return _legacy_C_ops.unbind(input, num, 'axis', axis)

    helper = LayerHelper("unbind", **locals())
    check_type(input, 'input', (Variable), 'unbind')
    dtype = helper.input_dtype()
    check_dtype(
        dtype, 'unbind', ['float32', 'float64', 'int32', 'int64'], 'unbind'
    )
    outs = [
        helper.create_variable_for_type_inference(dtype=helper.input_dtype())
        for i in range(num)
    ]
    helper.append_op(
        type="unbind",
        inputs={"X": input},
        outputs={"Out": outs},
        attrs={"axis": axis},
    )
    return outs


def scatter(x, index, updates, overwrite=True, name=None):
    """
    **Scatter Layer**
    Output is obtained by updating the input on selected indices based on updates.

    .. code-block:: python

        import numpy as np
        #input:
        x = np.array([[1, 1], [2, 2], [3, 3]])
        index = np.array([2, 1, 0, 1])
        # shape of updates should be the same as x
        # shape of updates with dim > 1 should be the same as input
        updates = np.array([[1, 1], [2, 2], [3, 3], [4, 4]])
        overwrite = False
        # calculation:
        if not overwrite:
            for i in range(len(index)):
                x[index[i]] = np.zeros((2))
        for i in range(len(index)):
            if (overwrite):
                x[index[i]] = updates[i]
            else:
                x[index[i]] += updates[i]
        # output:
        out = np.array([[3, 3], [6, 6], [1, 1]])
        out.shape # [3, 2]

    **NOTICE**: The order in which updates are applied is nondeterministic,
    so the output will be nondeterministic if index contains duplicates.

    Args:
        x (Tensor): The input N-D Tensor with ndim>=1. Data type can be float32, float64.
        index (Tensor): The index is a 1-D or 0-D Tensor. Data type can be int32, int64. The length of index cannot exceed updates's length, and the value in index cannot exceed input's length.
        updates (Tensor): Update input with updates parameter based on index. When the index is a 1-D tensor, the updates shape should be the same as input, and dim value with dim > 1 should be the same as input. When the index is a 0-D tensor, the updates should be a (N-1)-D tensor, the ith dim of the updates should be queal with the (i+1)th dim of the input.
        overwrite (bool): The mode that updating the output when there are same indices.

            If True, use the overwrite mode to update the output of the same index,
            if False, use the accumulate mode to update the output of the same index.Default value is True.

        name(str, optional): The default value is None. Normally there is no need for user to set this property.  For more information, please refer to :ref:`api_guide_Name` .

    Returns:
        Tensor, The output is a Tensor with the same shape as x.

    Examples:
        .. code-block:: python

            import paddle

            x = paddle.to_tensor([[1, 1], [2, 2], [3, 3]], dtype='float32')
            index = paddle.to_tensor([2, 1, 0, 1], dtype='int64')
            updates = paddle.to_tensor([[1, 1], [2, 2], [3, 3], [4, 4]], dtype='float32')

            output1 = paddle.scatter(x, index, updates, overwrite=False)
            # [[3., 3.],
            #  [6., 6.],
            #  [1., 1.]]

            output2 = paddle.scatter(x, index, updates, overwrite=True)
            # CPU device:
            # [[3., 3.],
            #  [4., 4.],
            #  [1., 1.]]
            # GPU device maybe have two results because of the repeated numbers in index
            # result 1:
            # [[3., 3.],
            #  [4., 4.],
            #  [1., 1.]]
            # result 2:
            # [[3., 3.],
            #  [2., 2.],
            #  [1., 1.]]
    """
    if in_dygraph_mode():
        return _C_ops.scatter(x, index, updates, overwrite)
    else:
        if _in_legacy_dygraph():
            return _legacy_C_ops.scatter(
                x, index, updates, 'overwrite', overwrite
            )
        else:
            check_variable_and_dtype(
                x,
                'dtype',
                ['float32', 'float64', 'float16', 'int32', 'int64'],
                'scatter',
            )
            check_type(overwrite, 'overwrite', bool, 'scatter')
            helper = LayerHelper('scatter', **locals())
            out = helper.create_variable_for_type_inference(x.dtype)
            helper.append_op(
                type="scatter",
                inputs={"X": x, "Ids": index, "Updates": updates},
                attrs={'overwrite': overwrite},
                outputs={"Out": out},
            )
            return out


@inplace_apis_in_dygraph_only
def scatter_(x, index, updates, overwrite=True, name=None):
    """
    Inplace version of ``scatter`` API, the output Tensor will be inplaced with input ``x``.
    Please refer to :ref:`api_paddle_tensor_scatter`.
    """
    if in_dygraph_mode():
        return _C_ops.scatter_(x, index, updates, overwrite)
    return _legacy_C_ops.scatter_(x, index, updates, 'overwrite', overwrite)


def scatter_nd_add(x, index, updates, name=None):
    r"""

    Output is obtained by applying sparse addition to a single value
    or slice in a Tensor.

    :attr:`x` is a Tensor with ndim :math:`R`
    and :attr:`index` is a Tensor with ndim :math:`K` . Thus, :attr:`index`
    has shape :math:`[i_0, i_1, ..., i_{K-2}, Q]` where :math:`Q \leq R` . :attr:`updates`
    is a Tensor with ndim :math:`K - 1 + R - Q` and its
    shape is :math:`index.shape[:-1] + x.shape[index.shape[-1]:]` .

    According to the :math:`[i_0, i_1, ..., i_{K-2}]` of :attr:`index` ,
    add the corresponding :attr:`updates` slice to the :attr:`x` slice
    which is obtained by the last one dimension of :attr:`index` .

    .. code-block:: text

        Given:

        * Case 1:
            x = [0, 1, 2, 3, 4, 5]
            index = [[1], [2], [3], [1]]
            updates = [9, 10, 11, 12]

          we get:

            output = [0, 22, 12, 14, 4, 5]

        * Case 2:
            x = [[65, 17], [-14, -25]]
            index = [[], []]
            updates = [[[-1, -2], [1, 2]],
                       [[3, 4], [-3, -4]]]
            x.shape = (2, 2)
            index.shape = (2, 0)
            updates.shape = (2, 2, 2)

          we get:

            output = [[67, 19], [-16, -27]]

    Args:
        x (Tensor): The x input. Its dtype should be int32, int64, float32, float64.
        index (Tensor): The index input with ndim > 1 and index.shape[-1] <= x.ndim.
                          Its dtype should be int32 or int64 as it is used as indexes.
        updates (Tensor): The updated value of scatter_nd_add op, and it must have the same dtype
                            as x. It must have the shape index.shape[:-1] + x.shape[index.shape[-1]:].
        name (str|None): The output tensor name. If set None, the layer will be named automatically.

    Returns:
        output (Tensor), The output is a tensor with the same shape and dtype as x.

    Examples:

        .. code-block:: python

            import paddle

            x = paddle.rand(shape=[3, 5, 9, 10], dtype='float32')
            updates = paddle.rand(shape=[3, 9, 10], dtype='float32')
            index = paddle.to_tensor([[1, 1],
                                    [0, 1],
                                    [1, 3]], dtype='int64')

            output = paddle.scatter_nd_add(x, index, updates)
            print(output.shape)
            # [3, 5, 9, 10]
    """
    if in_dygraph_mode():
        return _C_ops.scatter_nd_add(x, index, updates)
    else:
        if _in_legacy_dygraph():
            op = getattr(_legacy_C_ops, 'scatter_nd_add')
            return op(x, index, updates)
        else:
            if x.dtype != updates.dtype:
                raise ValueError("x and updates must have same data type.")

            helper = LayerHelper('scatter_nd_add', **locals())
            dtype = helper.input_dtype(input_param_name='x')
            output = helper.create_variable_for_type_inference(dtype)
            helper.append_op(
                type="scatter_nd_add",
                inputs={"X": x, "Index": index, "Updates": updates},
                outputs={"Out": output},
            )
            return output


def scatter_nd(index, updates, shape, name=None):
    """
    **Scatter_nd Layer**

    Output is obtained by scattering the :attr:`updates` in a new tensor according
    to :attr:`index` . This op is similar to :code:`scatter_nd_add`, except the
    tensor of :attr:`shape` is zero-initialized. Correspondingly, :code:`scatter_nd(index, updates, shape)`
    is equal to :code:`scatter_nd_add(paddle.zeros(shape, updates.dtype), index, updates)` .
    If :attr:`index` has repeated elements, then the corresponding updates are accumulated.
    Because of the numerical approximation issues, the different order of repeated elements
    in :attr:`index` may cause different results. The specific calculation method can be
    seen :code:`scatter_nd_add` . This op is the inverse of the :code:`gather_nd` op.

    Args:
        index (Tensor): The index input with ndim >= 1 and index.shape[-1] <= len(shape).
                          Its dtype should be int32 or int64 as it is used as indexes.
        updates (Tensor): The updated value of scatter_nd op. Its dtype should be float32, float64.
                            It must have the shape index.shape[:-1] + shape[index.shape[-1]:]
        shape(tuple|list): Shape of output tensor.
        name (str|None): The output Tensor name. If set None, the layer will be named automatically.

    Returns:
        output (Tensor), The output is a tensor with the same type as :attr:`updates` .

    Examples:

        .. code-block:: python

            import paddle

            index = paddle.to_tensor([[1, 1],
                                    [0, 1],
                                    [1, 3]], dtype="int64")
            updates = paddle.rand(shape=[3, 9, 10], dtype='float32')
            shape = [3, 5, 9, 10]

            output = paddle.scatter_nd(index, updates, shape)
    """
    return scatter_nd_add(zeros(shape, updates.dtype), index, updates, name)


def chunk(x, chunks, axis=0, name=None):
    """
    Split the input tensor into multiple sub-Tensors.

    Args:
        x (Tensor): A N-D Tensor. The data type is bool, float16, float32, float64, int32 or int64.
        chunks(int): The number of tensor to be split along the certain axis.
        axis (int|Tensor, optional): The axis along which to split, it can be a scalar with type
            ``int`` or a ``Tensor`` with shape [1] and data type  ``int32`` or ``int64``.
            If :math::`axis < 0`, the axis to split along is :math:`rank(x) + axis`. Default is 0.
        name (str, optional): The default value is None.  Normally there is no need for user to set this property.
            For more information, please refer to :ref:`api_guide_Name` .
    Returns:
        list(Tensor), The list of segmented Tensors.

    Examples:
        .. code-block:: python

            import paddle

            x = paddle.rand([3, 9, 5])

            out0, out1, out2 = paddle.chunk(x, chunks=3, axis=1)
            # out0.shape [3, 3, 5]
            # out1.shape [3, 3, 5]
            # out2.shape [3, 3, 5]


            # axis is negative, the real axis is (rank(x) + axis) which real
            # value is 1.
            out0, out1, out2 = paddle.chunk(x, chunks=3, axis=-2)
            # out0.shape [3, 3, 5]
            # out1.shape [3, 3, 5]
            # out2.shape [3, 3, 5]
    """
    check_type(chunks, 'chunks', (int), 'chunk')
    return split(x, num_or_sections=chunks, axis=axis, name=name)


def tile(x, repeat_times, name=None):
    """

    Construct a new Tensor by repeating ``x`` the number of times given by ``repeat_times``.
    After tiling, the value of the i'th dimension of the output is equal to ``x.shape[i]*repeat_times[i]``.

    Both the number of dimensions of ``x`` and the number of elements in ``repeat_times`` should be less than or equal to 6.

    Args:
        x (Tensor): The input tensor, its data type should be bool, float32, float64, int32 or int64.
        repeat_times (list|tuple|Tensor): The number of repeating times. If repeat_times is a list or tuple, all its elements
            should be integers or 1-D Tensors with the data type int32. If repeat_times is a Tensor, it should be an 1-D Tensor with the data type int32.
        name (str, optional): Name for the operation (optional, default is None). For more information, please refer to :ref:`api_guide_Name`.

    Returns:
        N-D Tensor. The data type is the same as ``x``. The size of the i-th dimension is equal to ``x[i] * repeat_times[i]``.

    Examples:
        .. code-block:: python

            import paddle

            data = paddle.to_tensor([1, 2, 3], dtype='int32')
            out = paddle.tile(data, repeat_times=[2, 1])
            print(out)
            # Tensor(shape=[2, 3], dtype=int32, place=Place(gpu:0), stop_gradient=True,
            #        [[1, 2, 3],
            #         [1, 2, 3]])

            out = paddle.tile(data, repeat_times=(2, 2))
            print(out)
            # Tensor(shape=[2, 6], dtype=int32, place=Place(gpu:0), stop_gradient=True,
            #        [[1, 2, 3, 1, 2, 3],
            #         [1, 2, 3, 1, 2, 3]])

            repeat_times = paddle.to_tensor([1, 2], dtype='int32')
            out = paddle.tile(data, repeat_times=repeat_times)
            print(out)
            # Tensor(shape=[1, 6], dtype=int32, place=Place(gpu:0), stop_gradient=True,
            #        [[1, 2, 3, 1, 2, 3]])
    """
    if in_dygraph_mode():
        if isinstance(repeat_times, core.eager.Tensor):
            assert (
                repeat_times.ndim == 1
            ), "Only support ndim == 1 while repeat_times is a Tensor."
            repeat_times = repeat_times.numpy().tolist()

        return _C_ops.tile(x, repeat_times)

    if _in_legacy_dygraph():
        return _legacy_C_ops.tile(x, 'repeat_times', repeat_times)

    check_type(repeat_times, 'repeat_times', (list, tuple, Variable), 'tile')
    if isinstance(repeat_times, Variable):
        assert (
            len(repeat_times.shape) == 1
        ), 'repeat_times must be an 1-D Tensor.'
    else:
        for elem in repeat_times:
            if isinstance(elem, Variable):
                assert (
                    len(elem.shape) == 1
                ), 'Elements in repeat_times must be 1-D Tensors or integers.'
            else:
                type_tuple = (int, np.int32, np.int64)
                assert isinstance(
                    elem, type_tuple
                ), 'Elements in repeat_times must be 1-D Tensors or integers.'

    check_variable_and_dtype(
        x, 'x', ['bool', 'float32', 'float64', 'int32', 'int64'], 'tile'
    )
    if convert_dtype(x.dtype) == 'bool' and not x.stop_gradient:
        raise ValueError(
            "When the date type is bool for the input 'x' of tile op, you "
            "must set its stop_gradient to be True by "
            "some_var.stop_gradient == True supporting some_var is the input."
        )

    helper = LayerHelper('tile', **locals())

    inputs = {"X": [x]}
    attrs = {}

    def get_attr_repeat_times(list_repeat_times):
        attrs_repeat_times = []
        for idx, times in enumerate(list_repeat_times):
            if isinstance(times, Variable):
                attrs_repeat_times.append(-1)
            else:
                attrs_repeat_times.append(times)
                assert (
                    times > 0
                ), "All elements in repeat_times must be positive for tile."
        return attrs_repeat_times

    if isinstance(repeat_times, Variable):
        repeat_times.stop_gradient = True
        inputs['RepeatTimes'] = repeat_times
        attrs['repeat_times'] = [-1]
    elif isinstance(repeat_times, (list, tuple)):
        attrs['repeat_times'] = get_attr_repeat_times(repeat_times)
        if utils._contain_var(repeat_times):
            inputs['repeat_times_tensor'] = utils._convert_to_tensor_list(
                repeat_times
            )

    dtype = helper.input_dtype(input_param_name='x')
    out = helper.create_variable_for_type_inference(dtype)
    helper.append_op(
        type='tile', inputs=inputs, outputs={'Out': out}, attrs=attrs
    )
    return out


def expand_as(x, y, name=None):
    """

    Expand the input tensor ``x`` to the same shape as the input tensor ``y``.

    Both the number of dimensions of ``x`` and ``y`` must be less than or equal to 6, and the number of dimensions of ``y`` must be greather than or equal to that of ``x``. The dimension to expand must have a value of 1.

    Args:
        x (Tensor): The input tensor, its data type is bool, float32, float64, int32 or int64.
        y (Tensor): The input tensor that gives the shape to expand to.
        name (str, optional): The default value is None. Normally there is no need for user to set this property. For more information, please refer to :ref:`api_guide_Name`.

    Returns:
        N-D Tensor, A Tensor with the same shape as ``y``. The data type is the same as ``x``.

    Examples:
        .. code-block:: python

            import paddle

            data_x = paddle.to_tensor([1, 2, 3], 'int32')
            data_y = paddle.to_tensor([[1, 2, 3], [4, 5, 6]], 'int32')
            out = paddle.expand_as(data_x, data_y)
            print(out)
            # Tensor(shape=[2, 3], dtype=int32, place=Place(gpu:0), stop_gradient=True,
            #        [[1, 2, 3],
            #         [1, 2, 3]])
    """
    if in_dygraph_mode():
        return _C_ops.expand_as(x, None, y.shape)

    if _non_static_mode():
        return _legacy_C_ops.expand_as_v2(x, 'target_shape', y.shape)

    check_variable_and_dtype(
        x, 'x', ['bool', 'float32', 'float64', 'int32', 'int64'], 'expand_as'
    )
    check_type(y, 'y', Variable, 'expand_as')

    if convert_dtype(x.dtype) == 'bool' and not x.stop_gradient:
        raise ValueError(
            "When the data type of input 'x' for expand_as is bool, "
            "you must set its stop_gradient to be False by "
            "some_var.stop_gradient = True, supporting "
            "some_var as the input 'x'."
        )
    inputs = {"X": [x], "Y": [y]}

    helper = LayerHelper('expand_as', **locals())
    dtype = helper.input_dtype(input_param_name='x')
    out = helper.create_variable_for_type_inference(dtype)
    helper.append_op(
        type='expand_as_v2',
        inputs=inputs,
        attrs={'target_shape': y.shape},
        outputs={'Out': out},
    )
    return out


def broadcast_to(x, shape, name=None):
    """

    Broadcast the input tensor to a given shape.

    Both the number of dimensions of ``x`` and the number of elements in ``shape`` should be less than or equal to 6. The dimension to broadcast to must have a value 1.


    Args:
        x (Tensor): The input tensor, its data type is bool, float32, float64, int32 or int64.
        shape (list|tuple|Tensor): The result shape after broadcasting. The data type is int32. If shape is a list or tuple, all its elements
            should be integers or 1-D Tensors with the data type int32. If shape is a Tensor, it should be an 1-D Tensor with the data type int32.
            The value -1 in shape means keeping the corresponding dimension unchanged.
        name (str, optional): Name for the operation (optional, default is None). For more information, please refer to :ref:`api_guide_Name`.
    Returns:
        N-D Tensor, A Tensor with the given shape. The data type is the same as ``x``.

    Examples:
        .. code-block:: python

            import paddle

            data = paddle.to_tensor([1, 2, 3], dtype='int32')
            out = paddle.broadcast_to(data, shape=[2, 3])
            print(out)
            # [[1, 2, 3], [1, 2, 3]]
    """
    if in_dygraph_mode():
        return _C_ops.expand(x, shape)
    if _in_legacy_dygraph():
        return _legacy_C_ops.expand_v2(x, 'shape', shape)

    if isinstance(shape, Variable):
        assert len(shape.shape) == 1, 'shape must be an 1-D Tensor.'
    else:
        for elem in shape:
            if isinstance(elem, Variable):
                assert (
                    len(elem.shape) == 1
                ), 'Elements in shape must be 1-D Tensors or integers.'
            else:
                type_tuple = (int, np.int32, np.int64)
                assert isinstance(
                    elem, type_tuple
                ), 'Elements in shape must be 1-D Tensors or integers.'

    check_variable_and_dtype(
        x, 'x', ['bool', 'float32', 'float64', 'int32', 'int64'], 'broadcast_to'
    )
    check_type(shape, 'shape', (list, tuple, Variable), 'broadcast_to')
    if convert_dtype(x.dtype) == 'bool' and not x.stop_gradient:
        raise ValueError(
            "When the data type of input 'x' for broadcast_to is bool, "
            "you must set its stop_gradient to be False by "
            "some_var.stop_gradient = True, supporting "
            "some_var as the input."
        )

    inputs = {"X": [x]}
    attrs = {}

    helper = LayerHelper('expand', **locals())

    def get_attr_expand_shape(list_expand_shape):
        attrs_expand_shape = []
        for idx, shape in enumerate(list_expand_shape):
            if isinstance(shape, Variable):
                attrs_expand_shape.append(-1)
            else:
                attrs_expand_shape.append(shape)
                assert (
                    shape > 0 or shape == -1
                ), "All elements in shape of broadcast_to must be positive or -1."
        return attrs_expand_shape

    if isinstance(shape, Variable):
        shape.stop_gradient = True
        inputs['Shape'] = shape
    elif isinstance(shape, (list, tuple)):
        attrs['shape'] = get_attr_expand_shape(shape)
        if utils._contain_var(shape):
            inputs['expand_shapes_tensor'] = utils._convert_to_tensor_list(
                shape
            )

    dtype = helper.input_dtype(input_param_name='x')
    out = helper.create_variable_for_type_inference(dtype)
    helper.append_op(
        type='expand_v2', inputs=inputs, outputs={'Out': out}, attrs=attrs
    )
    return out


def expand(x, shape, name=None):
    """

    Expand the input tensor to a given shape.

    Both the number of dimensions of ``x`` and the number of elements in ``shape`` should be less than or equal to 6. And the number of dimensions of ``x`` should be less than the number of elements in ``shape``. The dimension to expand must have a value 1.


    Args:
        x (Tensor): The input Tensor, its data type is bool, float32, float64, int32 or int64.
        shape (list|tuple|Tensor): The result shape after expanding. The data type is int32. If shape is a list or tuple, all its elements
            should be integers or 1-D Tensors with the data type int32. If shape is a Tensor, it should be an 1-D Tensor with the data type int32.
            The value -1 in shape means keeping the corresponding dimension unchanged.
        name (str, optional): The default value is None. Normally there is no need for user to set this property. For more information, please refer to :ref:`api_guide_Name` .

    Returns:
        N-D Tensor, A Tensor with the given shape. The data type is the same as ``x``.

    Examples:
        .. code-block:: python

            import paddle

            data = paddle.to_tensor([1, 2, 3], dtype='int32')
            out = paddle.expand(data, shape=[2, 3])
            print(out)
            # [[1, 2, 3], [1, 2, 3]]
    """
    if in_dygraph_mode():
        return _C_ops.expand(x, shape)

    if paddle.in_dynamic_mode():
        return _legacy_C_ops.expand_v2(x, 'shape', shape)

    if isinstance(shape, Variable):
        assert len(shape.shape) == 1, 'shape must be an 1-D Tensor.'
    else:
        for elem in shape:
            if isinstance(elem, Variable):
                assert (
                    len(elem.shape) == 1
                ), 'Elements in shape must be 1-D Tensors or integers.'
            else:
                type_tuple = (int, np.int32, np.int64)
                assert isinstance(
                    elem, type_tuple
                ), 'Elements in shape must be 1-D Tensors or integers.'

    check_variable_and_dtype(
        x,
        'x',
        ['bool', 'float16', 'float32', 'float64', 'int32', 'int64'],
        'expand',
    )
    check_type(shape, 'shape', (list, tuple, Variable), 'expand')
    if convert_dtype(x.dtype) == 'bool' and not x.stop_gradient:
        raise ValueError(
            "When the data type of input 'x' for expand is bool, "
            "you must set its stop_gradient to be False by "
            "some_var.stop_gradient = True, supporting "
            "some_var as the input."
        )

    inputs = {"X": [x]}
    attrs = {}

    helper = LayerHelper('expand', **locals())

    def get_attr_expand_shape(list_expand_shape):
        attrs_expand_shape = []
        for idx, shape in enumerate(list_expand_shape):
            if isinstance(shape, Variable):
                attrs_expand_shape.append(-2)
            else:
                attrs_expand_shape.append(shape)
                assert (
                    shape > 0 or shape == -1
                ), "All elements in shape of expand must be positive or -1."
        return attrs_expand_shape

    if isinstance(shape, Variable):
        shape.stop_gradient = True
        inputs['Shape'] = shape
    elif isinstance(shape, (list, tuple)):
        attrs['shape'] = get_attr_expand_shape(shape)
        if utils._contain_var(shape):
            inputs['expand_shapes_tensor'] = utils._convert_to_tensor_list(
                shape
            )

    dtype = helper.input_dtype(input_param_name='x')
    out = helper.create_variable_for_type_inference(dtype)
    helper.append_op(
        type='expand_v2', inputs=inputs, outputs={'Out': out}, attrs=attrs
    )
    return out


def reshape(x, shape, name=None):
    """
    Changes the shape of ``x`` without changing its data.

    Note that the output Tensor will share data with origin Tensor and doesn't
    have a Tensor copy in ``dygraph`` mode.
    If you want to use the Tensor copy version, please use `Tensor.clone` like
    ``reshape_clone_x = x.reshape([-1]).clone()``.

    Some tricks exist when specifying the target shape.

        - 1. -1 means the value of this dimension is inferred from the total element number of x and remaining dimensions. Thus one and only one dimension can be set -1.

        - 2. 0 means the actual dimension value is going to be copied from the corresponding dimension of x. The index of 0s in shape can not exceed the dimension of x.

    Here are some examples to explain it.

        - 1. Given a 3-D tensor x with a shape [2, 4, 6], and the target shape is [6, 8], the reshape operator will transform x into a 2-D tensor with shape [6, 8] and leaving x's data unchanged.

        - 2. Given a 3-D tensor x with a shape [2, 4, 6], and the target shape specified is [2, 3, -1, 2], the reshape operator will transform x into a 4-D tensor with shape [2, 3, 4, 2] and leaving x's data unchanged. In this case, one dimension of the target shape is set to -1, the value of this dimension is inferred from the total element number of x and remaining dimensions.

        - 3. Given a 3-D tensor x with a shape [2, 4, 6], and the target shape is [-1, 0, 3, 2], the reshape operator will transform x into a 4-D tensor with shape [2, 4, 3, 2] and leaving x's data unchanged. In this case, besides -1, 0 means the actual dimension value is going to be copied from the corresponding dimension of x.

    Args:
        x (Tensor): An N-D Tensor. The data type is ``float32``, ``float64``, ``int32``, ``int64`` or ``bool``
        shape (list|tuple|Tensor): Define the target shape. At most one dimension of the target shape can be -1.
                        The data type is ``int32`` . If ``shape`` is a list or tuple, the elements of it should be integers or Tensors with shape [1].
                        If ``shape`` is an Tensor, it should be an 1-D Tensor .
        name (str, optional): Name for the operation (optional, default is None). For more information, please refer to :ref:`api_guide_Name`.

    Returns:
        Tensor, A reshaped Tensor with the same data type as ``x``.

    Examples:
        .. code-block:: python

            import paddle

            x = paddle.rand([2, 4, 6], dtype="float32")
            positive_four = paddle.full([1], 4, "int32")

            out = paddle.reshape(x, [-1, 0, 3, 2])
            print(out)
            # the shape is [2,4,3,2].

            out = paddle.reshape(x, shape=[positive_four, 12])
            print(out)
            # the shape of out_2 is [4, 12].

            shape_tensor = paddle.to_tensor([8, 6], dtype=paddle.int32)
            out = paddle.reshape(x, shape=shape_tensor)
            print(out.shape)
            # the shape is [8, 6].
            # out shares data with x in dygraph mode
            x[0, 0, 0] = 10.
            print(out[0, 0])
            # the value is [10.]

    """
    actual_shape = None

    if in_dygraph_mode():
        tmp_tensor_type = core.eager.Tensor
        if isinstance(shape, (list, tuple)):
            shape = [
                item.numpy().item(0)
                if isinstance(item, tmp_tensor_type)
                else item
                for item in shape
            ]
            if shape == x.shape:
                out = x
            else:
                out = _C_ops.reshape(x, shape)
        elif isinstance(shape, core.eager.Tensor):
            shape.stop_gradient = True
            out = _C_ops.reshape(x, shape)
        else:
            raise ValueError(
                "shape must be an instance of `list`, `tuple` or `Variable`,"
                " got '{}.'".format(type(shape))
            )

        return out
    else:
        if _in_legacy_dygraph():
            tmp_tensor_type = Variable
            if isinstance(shape, (list, tuple)):
                shape = [
                    item.numpy().item(0) if isinstance(item, Variable) else item
                    for item in shape
                ]
                out, _ = _legacy_C_ops.reshape2(x, None, 'shape', shape)
            elif isinstance(shape, tmp_tensor_type):
                shape.stop_gradient = True
                out, _ = _legacy_C_ops.reshape2(x, shape)
            else:
                raise ValueError(
                    "shape must be an instance of `list`, `tuple` or `Variable`,"
                    " got '{}.'".format(type(shape))
                )

            return out

    check_variable_and_dtype(
        x,
        'x',
        [
            'float16',
            'float32',
            'float64',
            'int16',
            'int32',
            'int64',
            'bool',
            'uint16',
        ],
        'reshape',
    )
    check_type(shape, 'shape', (list, tuple, Variable), 'reshape')
    check_type(actual_shape, 'actual_shape', (Variable, type(None)), 'reshape')

    helper = LayerHelper("reshape2", **locals())

    def get_attr_shape(list_shape):
        unk_dim_idx = -1
        attrs_shape = []
        for dim_idx, dim_size in enumerate(list_shape):
            if isinstance(dim_size, Variable):
                attrs_shape.append(-1)
            else:
                attrs_shape.append(dim_size)
                if dim_size == -1:
                    assert unk_dim_idx == -1, (
                        "Only one dimension value of 'shape' in reshape can "
                        "be -1. But received shape[%d] is also -1.\n"
                        "\n\t# N = x.shape()[2]\t\t# N is an int. "
                        "(NOT recommend under @to_static)\n\tN = paddle.shape(x)[2]\t\t"
                        "# N is a Tensor. (Recommend)\n\tz = paddle.reshape([N, -1, 4])"
                        "\t# z.shape is [-1, -1, 4]\n\n"
                        "    If your target shape in Reshape represents dynamic shape, "
                        "please turn it into a Tensor under @to_static. See above example for details."
                        % dim_idx
                    )
                    unk_dim_idx = dim_idx
                elif dim_size == 0:
                    assert dim_idx < len(x.shape), (
                        "The index of 0 in `shape` must be less than "
                        "the input tensor X's dimensions. "
                        "But received shape[%d] = 0, X's dimensions = %d."
                        % (dim_idx, len(x.shape))
                    )
                else:
                    assert dim_size > 0, (
                        "Each dimension value of 'shape' in reshape must not "
                        "be negative except one unknown dimension. "
                        "But received shape[%d] = %s."
                        % (dim_idx, str(dim_size))
                    )
        return attrs_shape

    inputs = {"X": x}
    attrs = {}
    if isinstance(shape, Variable):
        shape.stop_gradient = True
        inputs["Shape"] = shape
    elif isinstance(shape, (list, tuple)):
        assert len(shape) > 0, (
            "The size of 'shape' in reshape can't be zero, "
            "but received %s." % len(shape)
        )
        attrs["shape"] = get_attr_shape(shape)
        if utils._contain_var(shape):
            inputs['ShapeTensor'] = utils._convert_to_tensor_list(shape)
        elif isinstance(actual_shape, Variable):
            actual_shape.stop_gradient = True
            inputs["Shape"] = actual_shape

    out = helper.create_variable_for_type_inference(dtype=x.dtype)
    x_shape = helper.create_variable_for_type_inference(dtype=x.dtype)
    helper.append_op(
        type="reshape2",
        inputs=inputs,
        attrs=attrs,
        outputs={"Out": out, "XShape": x_shape},
    )

    return out


@inplace_apis_in_dygraph_only
def reshape_(x, shape, name=None):
    """
    Inplace version of ``reshape`` API, the output Tensor will be inplaced with input ``x``.
    Please refer to :ref:`api_paddle_tensor_reshape`.
    """
    if in_dygraph_mode():
        tmp_tensor_type = core.eager.Tensor
        if isinstance(shape, (list, tuple)):
            shape = [
                item.numpy().item(0)
                if isinstance(item, tmp_tensor_type)
                else item
                for item in shape
            ]
            if shape == x.shape:
                out = x
            else:
                out = _C_ops.reshape_(x, shape)
        elif isinstance(shape, tmp_tensor_type):
            shape.stop_gradient = True
            out = _C_ops.reshape_(x, shape)
        else:
            raise ValueError(
                "shape must be an instance of `list`, `tuple` or `Variable`,"
                " got '{}.'".format(type(shape))
            )

        return out
    else:
        if isinstance(shape, (list, tuple)):
            shape = [
                item.numpy().item(0) if isinstance(item, Variable) else item
                for item in shape
            ]
            out, _ = _legacy_C_ops.reshape2_(x, None, 'shape', shape)
            return out
        elif isinstance(shape, Variable):
            shape.stop_gradient = True
            # NOTE(pangyoki): Cannot support the case where the shape Tensor
            # is negative. In the infer_shape stage, the input's dim will
            # be changed to a negative number.
            # Thus, convert Shape Tensor to list firstly and then call
            # reshape inplace op.
            shape_list = shape.numpy().tolist()
            out, _ = _legacy_C_ops.reshape2_(x, None, 'shape', shape_list)
            return out


def gather_nd(x, index, name=None):
    """

    This function is actually a high-dimensional extension of :code:`gather`
    and supports for simultaneous indexing by multiple axes. :attr:`index` is a
    K-dimensional integer tensor, which is regarded as a (K-1)-dimensional
    tensor of :attr:`index` into :attr:`input`, where each element defines
    a slice of params:

    .. math::

        output[(i_0, ..., i_{K-2})] = input[index[(i_0, ..., i_{K-2})]]

    Obviously, :code:`index.shape[-1] <= input.rank` . And, the output tensor has
    shape :code:`index.shape[:-1] + input.shape[index.shape[-1]:]` .

    .. code-block:: text

            Given:
                x =  [[[ 0,  1,  2,  3],
                       [ 4,  5,  6,  7],
                       [ 8,  9, 10, 11]],
                      [[12, 13, 14, 15],
                       [16, 17, 18, 19],
                       [20, 21, 22, 23]]]
                x.shape = (2, 3, 4)

            * Case 1:
                index = [[1]]

                gather_nd(x, index)
                         = [x[1, :, :]]
                         = [[12, 13, 14, 15],
                            [16, 17, 18, 19],
                            [20, 21, 22, 23]]

            * Case 2:
                index = [[0,2]]

                gather_nd(x, index)
                         = [x[0, 2, :]]
                         = [8, 9, 10, 11]

            * Case 3:
                index = [[1, 2, 3]]

                gather_nd(x, index)
                         = [x[1, 2, 3]]
                         = [23]

    Args:
        x (Tensor): The input Tensor which it's data type should be bool, float32, float64, int32, int64.
        index (Tensor): The index input with rank > 1, index.shape[-1] <= input.rank.
                        Its dtype should be int32, int64.
        name (str, optional): Name for the operation (optional, default is None). For more information, please refer to :ref:`api_guide_Name`.

    Returns:
        output (Tensor), A tensor with the shape index.shape[:-1] + input.shape[index.shape[-1]:]

    Examples:

        .. code-block:: python

            import paddle

            x = paddle.to_tensor([[[1, 2], [3, 4], [5, 6]],
                                  [[7, 8], [9, 10], [11, 12]]])
            index = paddle.to_tensor([[0, 1]])

            output = paddle.gather_nd(x, index) #[[3, 4]]

    """
    if in_dygraph_mode():
        return _C_ops.gather_nd(x, index)
    else:
        if _in_legacy_dygraph():
            return _legacy_C_ops.gather_nd(x, index)
    check_variable_and_dtype(
        x,
        'x',
        ['bool', 'float32', 'float64', 'int16', 'int32', 'int64'],
        'gather_np',
    )
    check_variable_and_dtype(index, 'index', ['int32', 'int64'], 'gather_np')
    helper = LayerHelper('gather_nd', **locals())
    dtype = helper.input_dtype()
    output = helper.create_variable_for_type_inference(dtype)
    helper.append_op(
        type="gather_nd",
        inputs={"X": x, "Index": index},
        outputs={"Out": output},
    )
    return output


def strided_slice(x, axes, starts, ends, strides, name=None):
    """
    This operator produces a slice of ``x`` along multiple axes. Similar to numpy:
    https://docs.scipy.org/doc/numpy/reference/arrays.indexing.html
    Slice uses ``axes``, ``starts`` and ``ends`` attributes to specify the start and
    end dimension for each axis in the list of axes and Slice uses this information
    to slice the input data tensor. If a negative value is passed to
    ``starts`` or ``ends`` such as :math:`-i`,  it represents the reverse position of the
    axis :math:`i-1` th(here 0 is the initial position). The ``strides`` represents steps of
    slicing and if the ``strides`` is negative, slice operation is in the opposite direction.
    If the value passed to ``starts`` or ``ends`` is greater than n
    (the number of elements in this dimension), it represents n.
    For slicing to the end of a dimension with unknown size, it is recommended
    to pass in INT_MAX. The size of ``axes`` must be equal to ``starts`` , ``ends`` and ``strides``.
    Following examples will explain how strided_slice works:

    .. code-block:: text

        Case1:
            Given:
                data = [ [1, 2, 3, 4], [5, 6, 7, 8], ]
                axes = [0, 1]
                starts = [1, 0]
                ends = [2, 3]
                strides = [1, 1]
            Then:
                result = [ [5, 6, 7], ]

        Case2:
            Given:
                data = [ [1, 2, 3, 4], [5, 6, 7, 8], ]
                axes = [0, 1]
                starts = [0, 1]
                ends = [2, 0]
                strides = [1, -1]
            Then:
                result = [ [8, 7, 6], ]
        Case3:
            Given:
                data = [ [1, 2, 3, 4], [5, 6, 7, 8], ]
                axes = [0, 1]
                starts = [0, 1]
                ends = [-1, 1000]
                strides = [1, 3]
            Then:
                result = [ [2], ]

    Args:
        x (Tensor): An N-D ``Tensor``. The data type is ``bool``, ``float16``, ``float32``, ``float64``, ``int32`` or ``int64``.
        axes (list|tuple): The data type is ``int32`` . Axes that `starts` and `ends` apply to.
                            It's optional. If it is not provides, it will be treated as :math:`[0,1,...,len(starts)-1]`.
        starts (list|tuple|Tensor): The data type is ``int32`` . If ``starts`` is a list or tuple, the elements of                                                                                          it should be integers or Tensors with shape [1]. If ``starts`` is an Tensor, it should be an 1-D Tensor.                                                                                    It represents starting indices of corresponding axis in ``axes``.
        ends (list|tuple|Tensor): The data type is ``int32`` . If ``ends`` is a list or tuple, the elements of
                it should be integers or Tensors with shape [1]. If ``ends`` is an Tensor, it should be an 1-D Tensor .                                                                                     It represents ending indices of corresponding axis in ``axes``.
        strides (list|tuple|Tensor): The data type is ``int32`` . If ``strides`` is a list or tuple, the elements of
                it should be integers or Tensors with shape [1]. If ``strides`` is an Tensor, it should be an 1-D Tensor .                                                                                  It represents slice step of corresponding axis in ``axes``.
        name(str, optional): The default value is None.  Normally there is no need for user to set this property.
                        For more information, please refer to :ref:`api_guide_Name` .

    Returns:
        Tensor, A ``Tensor`` with the same dimension as ``x``. The data type is same as ``x``.

    Examples:
        .. code-block:: python

            import paddle
            x = paddle.zeros(shape=[3,4,5,6], dtype="float32")
            # example 1:
            # attr starts is a list which doesn't contain Tensor.
            axes = [1, 2, 3]
            starts = [-3, 0, 2]
            ends = [3, 2, 4]
            strides_1 = [1, 1, 1]
            strides_2 = [1, 1, 2]
            sliced_1 = paddle.strided_slice(x, axes=axes, starts=starts, ends=ends, strides=strides_1)
            # sliced_1 is x[:, 1:3:1, 0:2:1, 2:4:1].
            # example 2:
            # attr starts is a list which contain tensor Tensor.
            minus_3 = paddle.full(shape=[1], fill_value=-3, dtype='int32')
            sliced_2 = paddle.strided_slice(x, axes=axes, starts=[minus_3, 0, 2], ends=ends, strides=strides_2)
            # sliced_2 is x[:, 1:3:1, 0:2:1, 2:4:2].
    """
    if in_dygraph_mode():
        return _C_ops.strided_slice(x, axes, starts, ends, strides)

    helper = LayerHelper('strided_slice', **locals())

    check_variable_and_dtype(
        x,
        'x',
        ['bool', 'float16', 'float32', 'float64', 'int32', 'int64'],
        'strided_slice',
    )
    check_type(axes, 'axes', (list, tuple), 'strided_slice')
    check_type(starts, 'starts', (list, tuple, Variable), 'strided_slice')
    check_type(ends, 'ends', (list, tuple, Variable), 'strided_slice')
    check_type(strides, 'strides', (list, tuple, Variable), 'strided_slice')

    def check_list_elements_dtype(list_input, input_name):
        if isinstance(list_input, Variable):
            check_dtype(
                list_input.dtype, input_name, ['int32'], 'strided_slice'
            )
        else:
            for i, var in enumerate(list_input):
                var_name = input_name + '[' + str(i) + ']'
                if isinstance(var, Variable):
                    check_dtype(var.dtype, var_name, ['int32'], 'strided_slice')

    check_list_elements_dtype(axes, 'axes')
    check_list_elements_dtype(starts, 'starts')
    check_list_elements_dtype(ends, 'ends')
    check_list_elements_dtype(strides, 'strides')

    def get_new_list_tensor(old_list):
        new_list_tensor = []
        for dim in old_list:
            if isinstance(dim, Variable):
                dim.stop_gradient = True
                new_list_tensor.append(dim)
            else:
                assert isinstance(dim, int)
                temp_out = helper.create_variable_for_type_inference('int32')
                fill_constant([1], 'int32', dim, force_cpu=True, out=temp_out)
                new_list_tensor.append(temp_out)
        return new_list_tensor

    inputs = {'Input': x}
    attrs = {'axes': axes}
    infer_flags = list(1 for i in range(len(axes)))

    if _in_legacy_dygraph():
        inputs = {'Input': x}
        attrs = {
            'axes': axes,
            'starts': starts,
            'ends': ends,
            'strides': strides,
            'infer_flags': infer_flags,
        }
    else:
        # starts
        if isinstance(starts, Variable):
            starts.stop_gradient = True
            inputs['StartsTensor'] = starts
        elif isinstance(starts, (list, tuple)):
            attrs['starts'] = []
            if utils._contain_var(starts):
                inputs['StartsTensorList'] = get_new_list_tensor(starts)
                for i, dim in enumerate(starts):
                    if isinstance(dim, Variable):
                        attrs['starts'].append(-1)
                        infer_flags[i] = -1
                    else:
                        attrs['starts'].append(dim)
            else:
                attrs['starts'] = starts

        # ends
        if isinstance(ends, Variable):
            ends.stop_gradient = True
            inputs['EndsTensor'] = ends
        elif isinstance(ends, (list, tuple)):
            attrs['ends'] = []
            if utils._contain_var(ends):
                inputs['EndsTensorList'] = get_new_list_tensor(ends)
                for i, dim in enumerate(ends):
                    if isinstance(dim, Variable):
                        attrs['ends'].append(-1)
                        infer_flags[i] = -1
                    else:
                        attrs['ends'].append(dim)
            else:
                attrs['ends'] = ends

        # strides
        if isinstance(strides, Variable):
            strides.stop_gradient = True
            inputs['StridesTensor'] = strides
        elif isinstance(strides, (list, tuple)):
            attrs['strides'] = []
            if utils._contain_var(strides):
                inputs['StridesTensorList'] = get_new_list_tensor(strides)
                for i, dim in enumerate(strides):
                    if isinstance(dim, Variable):
                        attrs['strides'].append(-1)
                        infer_flags[i] = -1
                    else:
                        attrs['strides'].append(dim)
            else:
                attrs['strides'] = strides
        attrs['infer_flags'] = infer_flags
    out = helper.create_variable_for_type_inference(
        dtype=helper.input_dtype('x')
    )
    helper.append_op(
        type='strided_slice', inputs=inputs, attrs=attrs, outputs={'Out': out}
    )

    return out


def tensordot(x, y, axes=2, name=None):
    r"""
    This function computes a contraction, which sum the product of elements from two tensors along the given axes.

    Args:
        x (Tensor): The left tensor for contraction with data type ``float32`` or ``float64``.
        y (Tensor): The right tensor for contraction with the same data type as ``x``.
        axes (int|tuple|list|Tensor, optional):  The axes to contract for ``x`` and ``y``, defaulted to integer ``2``.

            1. It could be a non-negative integer ``n``,
               in which the function will sum over the last ``n`` axes of ``x`` and the first ``n`` axes of ``y`` in order.

            2. It could be a 1-d tuple or list with data type ``int``, in which ``x`` and ``y`` will be contracted along the same given axes.
               For example, ``axes`` =[0, 1] applies contraction along the first two axes for ``x`` and the first two axes for ``y``.

            3. It could be a tuple or list containing one or two 1-d tuple|list|Tensor with data type ``int``.
               When containing one tuple|list|Tensor, the data in tuple|list|Tensor specified the same axes for ``x`` and ``y`` to contract.
               When containing two tuple|list|Tensor, the first will be applied to ``x`` and the second to ``y``.
               When containing more than two tuple|list|Tensor, only the first two axis sequences will be used while the others will be ignored.

            4. It could be a tensor, in which the ``axes`` tensor will be translated to a python list
               and applied the same rules described above to determine the contraction axes.
               Note that the ``axes`` with Tensor type is ONLY available in Dygraph mode.
        name(str, optional): The default value is None.  Normally there is no need for user to set this property.
                             For more information, please refer to :ref:`api_guide_Name` .

    Return:
        Output (Tensor), The contraction result with the same data type as ``x`` and ``y``.
        In general, :math:`output.ndim = x.ndim + y.ndim - 2 \times n_{axes}`, where :math:`n_{axes}` denotes the number of axes to be contracted.

    NOTES:
        1. This function supports tensor broadcast,
           the size in the corresponding dimensions of ``x`` and ``y`` should be equal, or applies to the broadcast rules.
        2. This function also supports axes expansion,
           when the two given axis sequences for ``x`` and ``y`` are of different lengths,
           the shorter sequence will expand the same axes as the longer one at the end.
           For example, if ``axes`` =[[0, 1, 2, 3], [1, 0]],
           the axis sequence for ``x`` is [0, 1, 2, 3],
           while the corresponding axis sequences for ``y`` will be expanded from [1, 0] to [1, 0, 2, 3].

    Examples:
        .. code-block:: python

            import paddle

            data_type = 'float64'

            # For two 2-d tensor x and y, the case axes=0 is equivalent to outer product.
            # Note that tensordot supports empty axis sequence, so all the axes=0, axes=[], axes=[[]], and axes=[[],[]] are equivalent cases.
            x = paddle.arange(4, dtype=data_type).reshape([2, 2])
            y = paddle.arange(4, dtype=data_type).reshape([2, 2])
            z = paddle.tensordot(x, y, axes=0)
            # z = [[[[0., 0.],
            #        [0., 0.]],
            #
            #       [[0., 1.],
            #        [2., 3.]]],
            #
            #
            #      [[[0., 2.],
            #        [4., 6.]],
            #
            #       [[0., 3.],
            #        [6., 9.]]]]


            # For two 1-d tensor x and y, the case axes=1 is equivalent to inner product.
            x = paddle.arange(10, dtype=data_type)
            y = paddle.arange(10, dtype=data_type)
            z1 = paddle.tensordot(x, y, axes=1)
            z2 = paddle.dot(x, y)
            # z1 = z2 = [285.]


            # For two 2-d tensor x and y, the case axes=1 is equivalent to matrix multiplication.
            x = paddle.arange(6, dtype=data_type).reshape([2, 3])
            y = paddle.arange(12, dtype=data_type).reshape([3, 4])
            z1 = paddle.tensordot(x, y, axes=1)
            z2 = paddle.matmul(x, y)
            # z1 = z2 =  [[20., 23., 26., 29.],
            #             [56., 68., 80., 92.]]


            # When axes is a 1-d int list, x and y will be contracted along the same given axes.
            # Note that axes=[1, 2] is equivalent to axes=[[1, 2]], axes=[[1, 2], []], axes=[[1, 2], [1]], and axes=[[1, 2], [1, 2]].
            x = paddle.arange(24, dtype=data_type).reshape([2, 3, 4])
            y = paddle.arange(36, dtype=data_type).reshape([3, 3, 4])
            z = paddle.tensordot(x, y, axes=[1, 2])
            # z =  [[506. , 1298., 2090.],
            #       [1298., 3818., 6338.]]


            # When axes is a list containing two 1-d int list, the first will be applied to x and the second to y.
            x = paddle.arange(60, dtype=data_type).reshape([3, 4, 5])
            y = paddle.arange(24, dtype=data_type).reshape([4, 3, 2])
            z = paddle.tensordot(x, y, axes=([1, 0], [0, 1]))
            # z =  [[4400., 4730.],
            #       [4532., 4874.],
            #       [4664., 5018.],
            #       [4796., 5162.],
            #       [4928., 5306.]]


            # Thanks to the support of axes expansion, axes=[[0, 1, 3, 4], [1, 0, 3, 4]] can be abbreviated as axes= [[0, 1, 3, 4], [1, 0]].
            x = paddle.arange(720, dtype=data_type).reshape([2, 3, 4, 5, 6])
            y = paddle.arange(720, dtype=data_type).reshape([3, 2, 4, 5, 6])
            z = paddle.tensordot(x, y, axes=[[0, 1, 3, 4], [1, 0]])
            # z = [[23217330., 24915630., 26613930., 28312230.],
            #      [24915630., 26775930., 28636230., 30496530.],
            #      [26613930., 28636230., 30658530., 32680830.],
            #      [28312230., 30496530., 32680830., 34865130.]]
    """
    op_type = 'tensordot'
    input_dtype = ['float32', 'float64']

    check_variable_and_dtype(x, 'x', input_dtype, op_type)
    check_variable_and_dtype(y, 'y', input_dtype, op_type)
    check_type(axes, 'axes', (int, tuple, list, Variable), op_type)

    def _var_to_list(var):
        if paddle.in_dynamic_mode():
            return tolist(var)
        raise TypeError(
            "The 'axes' with type 'Tensor' in "
            + op_type
            + " is not available in static graph mode, "
            "please convert its type to int|Tuple|List, or use dynamic graph mode."
        )

    axes_x = []
    axes_y = []
    if np.issubdtype(type(axes), np.integer):
        assert axes >= 0, (
            "The 'axes' in "
            + op_type
            + f" should not be negative, but received axes={axes}."
        )
        axes_x = range(x.ndim - axes, x.ndim)
        axes_y = range(axes)
    else:
        if isinstance(axes, Variable):
            axes = _var_to_list(axes)

        if not axes or np.issubdtype(type(axes[0]), np.integer):
            axes_x = axes
        else:
            axes_x = axes[0]
            if len(axes) > 1:
                axes_y = axes[1]

            if isinstance(axes_x, Variable):
                axes_x = _var_to_list(axes_x)
            if isinstance(axes_y, Variable):
                axes_y = _var_to_list(axes_y)

    axes_x, axes_y = list(axes_x), list(axes_y)
    len_axes_x, len_axes_y = len(axes_x), len(axes_y)
    if len_axes_x < len_axes_y:
        axes_x.extend(axes_y[len_axes_x:])
    elif len_axes_y < len_axes_x:
        axes_y.extend(axes_x[len_axes_y:])

    shape_x, shape_y = list(x.shape), list(y.shape)
    need_contracted_dim_x = np.zeros((x.ndim), dtype=bool)
    need_contracted_dim_y = np.zeros((y.ndim), dtype=bool)
    contraction_size = 1
    for i in range(len(axes_x)):
        dim_x, dim_y = axes_x[i], axes_y[i]
        sx, sy = shape_x[dim_x], shape_y[dim_y]
        if sx == 1:
            shape_y[dim_y] = 1
            y = y.sum(dim_y).reshape(shape_y)
        elif sy == 1:
            shape_x[dim_x] = 1
            x = x.sum(dim_x).reshape(shape_x)
        else:
            assert sx == sy, (
                "The dimensional size for 'x' and 'y' in "
                + op_type
                + f" should match each other, but 'x' has size {sx} in dim {dim_x} while 'y' has size {sy} in dim {dim_y}."
            )

        need_contracted_dim_x[dim_x] = True
        need_contracted_dim_y[dim_y] = True
        contraction_size *= shape_x[dim_x]

    perm_x = []
    perm_y = []
    shape_out = []
    not_contraction_size_x = 1
    not_contraction_size_y = 1
    for i in range(x.ndim):
        if not need_contracted_dim_x[i]:
            perm_x.append(i)
            shape_out.append(shape_x[i])
            not_contraction_size_x *= shape_x[i]
    perm_x.extend(axes_x)
    perm_y.extend(axes_y)
    for i in range(y.ndim):
        if not need_contracted_dim_y[i]:
            perm_y.append(i)
            shape_out.append(shape_y[i])
            not_contraction_size_y *= shape_y[i]

    if not shape_out:
        shape_out = [1]

    x = x.transpose(perm=perm_x).reshape(
        [not_contraction_size_x, contraction_size]
    )
    y = y.transpose(perm=perm_y).reshape(
        [contraction_size, not_contraction_size_y]
    )
    out = x.matmul(y).reshape(shape_out)
    return out


def as_complex(x, name=None):
    """Transform a real tensor to a complex tensor.

    The data type of the input tensor is 'float32' or 'float64', and the data
    type of the returned tensor is 'complex64' or 'complex128', respectively.

    The shape of the input tensor is ``(* ,2)``, (``*`` means arbitary shape), i.e.
    the size of the last axis shoule be 2, which represent the real and imag part
    of a complex number. The shape of the returned tensor is ``(*,)``.

    Args:
        x (Tensor): The input tensor. Data type is 'float32' or 'float64'.
        name (str, optional): Name for the operation (optional, default is None). For more information, please refer to :ref:`api_guide_Name`.

    Returns:
        Tensor, The output. Data type is 'complex64' or 'complex128', with the same precision as the input.

    Examples:
        .. code-block:: python

            import paddle
            x = paddle.arange(12, dtype=paddle.float32).reshape([2, 3, 2])
            y = paddle.as_complex(x)
            print(y)

            # Tensor(shape=[2, 3], dtype=complex64, place=Place(gpu:0), stop_gradient=True,
            #        [[1j      , (2+3j)  , (4+5j)  ],
            #         [(6+7j)  , (8+9j)  , (10+11j)]])
    """
    if in_dygraph_mode():
        return _C_ops.as_complex(x)
    if _in_legacy_dygraph():
        return _legacy_C_ops.as_complex(x)

    check_variable_and_dtype(x, 'x', ['float32', 'float64'], 'as_complex')
    op_type = "as_complex"
    helper = LayerHelper(op_type, **locals())
    inputs = {"X": x}
    out = helper.create_variable_for_type_inference(
        dtype=_real_to_complex_dtype(x.dtype)
    )
    outputs = {"Out": out}
    attrs = {}
    helper.append_op(type=op_type, inputs=inputs, attrs=attrs, outputs=outputs)
    return out


def as_real(x, name=None):
    """Transform a complex tensor to a real tensor.

    The data type of the input tensor is 'complex64' or 'complex128', and the data
    type of the returned tensor is 'float32' or 'float64', respectively.

    When the shape of the input tensor is ``(*, )``, (``*`` means arbitary shape),
    the shape of the output tensor is ``(*, 2)``, i.e. the shape of the output is
    the shape of the input appended by an extra ``2``.

    Args:
        x (Tensor): The input tensor. Data type is 'complex64' or 'complex128'.
        name (str, optional): Name for the operation (optional, default is None). For more information, please refer to :ref:`api_guide_Name`.

    Returns:
        Tensor, The output. Data type is 'float32' or 'float64', with the same precision as the input.

    Examples:
        .. code-block:: python

            import paddle
            x = paddle.arange(12, dtype=paddle.float32).reshape([2, 3, 2])
            y = paddle.as_complex(x)
            z = paddle.as_real(y)
            print(z)

            # Tensor(shape=[2, 3, 2], dtype=float32, place=Place(gpu:0), stop_gradient=True,
            #        [[[0. , 1. ],
            #          [2. , 3. ],
            #          [4. , 5. ]],

            #         [[6. , 7. ],
            #          [8. , 9. ],
            #          [10., 11.]]])
    """
    if in_dygraph_mode():
        return _C_ops.as_real(x)
    if _in_legacy_dygraph():
        return _legacy_C_ops.as_real(x)

    check_variable_and_dtype(x, 'x', ['complex64', 'complex128'], 'as_real')
    op_type = "as_real"
    helper = LayerHelper(op_type, **locals())
    inputs = {"X": x}
    out = helper.create_variable_for_type_inference(
        dtype=_complex_to_real_dtype(x.dtype)
    )
    outputs = {"Out": out}
    helper.append_op(type=op_type, inputs=inputs, outputs=outputs)
    return out


def repeat_interleave(x, repeats, axis=None, name=None):
    """

    Returns a new tensor which repeats the ``x`` tensor along dimension ``axis`` using
    the entries in ``repeats`` which is a int or a Tensor.

    Args:
        x (Tensor): The input Tensor to be operated. The data of ``x`` can be one of float32, float64, int32, int64.
        repeats (Tensor or int): The number of repetitions for each element. repeats is broadcasted to fit the shape of the given axis.
        axis (int, optional): The dimension in which we manipulate. Default: None, the output tensor is flatten.
        name(str, optional): The default value is None. Normally there is no
            need for user to set this property. For more information, please
            refer to :ref:`api_guide_Name`.

    Returns:
        Tensor, A Tensor with same data type as ``x``.

    Examples:
        .. code-block:: python

            import paddle

            x = paddle.to_tensor([[1, 2, 3], [4, 5, 6]])
            repeats  = paddle.to_tensor([3, 2, 1], dtype='int32')

            paddle.repeat_interleave(x, repeats, 1)
            # [[1, 1, 1, 2, 2, 3],
            #  [4, 4, 4, 5, 5, 6]]

            paddle.repeat_interleave(x, 2, 0)
            # [[1, 2, 3], [1, 2, 3], [4, 5, 6], [4, 5, 6]]

            paddle.repeat_interleave(x, 2, None)
            # [1, 1, 2, 2, 3, 3, 4, 4, 5, 5, 6, 6]
    """

    if axis is None:
        x = paddle.flatten(x)
        axis = 0

    if in_dygraph_mode():
        if isinstance(repeats, Variable):
            return _C_ops.repeat_interleave_with_tensor_index(x, repeats, axis)
        return _C_ops.repeat_interleave(x, repeats, axis)

    helper = LayerHelper("repeat_interleave", **locals())
    check_variable_and_dtype(
        x,
        'x',
        ['float32', 'float64', 'int32', 'int64'],
        'paddle.tensor.manipulation.repeat_interleave',
    )

    out = helper.create_variable_for_type_inference(x.dtype)

    helper.append_op(
        type='repeat_interleave',
        inputs={
            'X': x,
            'RepeatsTensor': repeats if isinstance(repeats, Variable) else None,
        },
        outputs={'Out': out},
        attrs={
            'dim': axis,
            'Repeats': repeats if isinstance(repeats, int) else 0,
        },
    )
    return out


def moveaxis(x, source, destination, name=None):
    """
    Move the axis of tensor from ``source`` position to ``destination`` position.

    Other axis that have not been moved remain their original order.

    Args:
        x (Tensor): The input Tensor. It is a N-D Tensor of data types bool, int32, int64, float32, float64, complex64, complex128.
        source(int|tuple|list): ``source`` position of axis that will be moved. Each element must be unique and integer.
        destination(int|tuple|list(int)): ``destination`` position of axis that has been moved. Each element must be unique and integer.
        name(str, optional): The default value is None.  Normally there is no need for user to set this
            property. For more information, please refer to :ref:`api_guide_Name`.

    Returns:
        Tensor, A new tensor whose axis have been moved.

    Examples:
        .. code-block:: python

            import paddle

            x = paddle.ones([3, 2, 4])
            paddle.moveaxis(x, [0, 1], [1, 2]).shape
            # [4, 3, 2]

            x = paddle.ones([2, 3])
            paddle.moveaxis(x, 0, 1).shape # equivalent to paddle.t(x)
            # [3, 2]
    """
    src = [source] if isinstance(source, int) else source
    dst = [destination] if isinstance(destination, int) else destination

    assert len(src) == len(
        dst
    ), "'source' must have the same number with 'destination'"

    count = Counter(src).most_common(1)
    if count[0][1] > 1:
        raise ValueError("Each elemment of 'source' must be unique!")
    count = Counter(dst).most_common(1)
    if count[0][1] > 1:
        raise ValueError("Each elemment of 'destination' must be unique!")

    ndim = len(x.shape)

    # perm is the new order after move axis
    perm = list(range(ndim))
    src_dims = list(range(ndim))
    dst_dims = list(range(ndim))

    for i, axis in enumerate(zip(src, dst)):
        assert isinstance(
            axis[0], int
        ), "Each elemment of 'source' must be integer."
        if axis[0] < 0:
            assert (
                axis[0] >= -ndim
            ), "'source' must be in the range of [-{0}, {0})".format(ndim)
            src[i] += ndim
        else:
            assert (
                axis[0] < ndim
            ), "'source' must be in the range of [-{0}, {0})".format(ndim)

        assert isinstance(
            axis[1], int
        ), "Each elemment of 'source' must be integer."
        if axis[1] < 0:
            assert (
                axis[1] >= -ndim
            ), "'source' must be in the range of [-{0}, {0})".format(ndim)
            dst[i] += ndim
        else:
            assert (
                axis[1] < ndim
            ), "'source' must be in the range of [-{0}, {0})".format(ndim)
        perm[dst[i]] = src[i]
        src_dims.remove(src[i])
        dst_dims.remove(dst[i])

    for i in range(len(src_dims)):
        perm[dst_dims[i]] = src_dims[i]

    if in_dygraph_mode():
        out = _C_ops.transpose(x, perm)
        return out

    if _in_legacy_dygraph():
        out, _ = _legacy_C_ops.transpose2(x, 'axis', perm)
        return out

    check_variable_and_dtype(
        x,
        'x',
        [
            'bool',
            'float16',
            'float32',
            'float64',
            'int32',
            'int64',
            'complex64',
            'complex128',
        ],
        'moveaxis',
    )

    helper = LayerHelper('moveaxis', **locals())
    out = helper.create_variable_for_type_inference(x.dtype)
    x_shape = helper.create_variable_for_type_inference(x.dtype)
    helper.append_op(
        type='transpose2',
        inputs={'X': [x]},
        outputs={'Out': [out], 'XShape': [x_shape]},
        attrs={'axis': perm},
    )
    return out


def non_negative_axis(arr, axis):
    ndim = len(arr.shape)
    if axis >= 0:
        assert (
            axis < ndim
        ), "'axis'  must be in the range of [-{0}, {0})".format(ndim)
    else:
        assert (
            axis >= -ndim
        ), "'axis'  must be in the range of [-{0}, {0})".format(ndim)
        axis += ndim

    return axis


def infer_broadcast_shape(arr, indices, axis):
    # This function is used in take/put_along_axis
    broadcast_shape_list = list(arr.shape)
    broadcast_shape_list[axis] = list(indices.shape)[axis]
    broadcast_shape = tuple(broadcast_shape_list)
    for i in range(len(arr.shape)):
        if arr.shape[i] < indices.shape[i]:
            # if indices matrix has larger size than arr matrix, do not broadcast.
            return None
    return broadcast_shape


def take_along_axis(arr, indices, axis):
    """
    Take values from the input array by given indices matrix along the designated axis.

    Args:
        arr (Tensor) : The input Tensor. Supported data types are float32 and float64.
        indices (Tensor) : Indices to take along each 1d slice of arr. This must match the dimension of arr,
            and need to broadcast against arr. Supported data type are int and int64.
        axis (int) : The axis to take 1d slices along.

    Returns:
        Tensor, The indexed element, same dtype with arr

    Examples:
        .. code-block:: python

            import paddle

            x = paddle.to_tensor([[1, 2, 3], [4, 5, 6], [7,8,9]])
            index = paddle.to_tensor([[0]])
            axis = 0
            result = paddle.take_along_axis(x, index, axis)
            print(result)
            # [[1, 2, 3]]
    """
    if len(arr.shape) != len(indices.shape):
        raise ValueError(
            "`indices` and `arr` must have the same number of dimensions!"
        )
    axis = non_negative_axis(arr, axis)
    broadcast_shape = infer_broadcast_shape(arr, indices, axis)
    if not broadcast_shape:
        # if indices matrix have larger size than arr, arr should broadcast into indices shape.
        broadcast_shape = indices.shape
    if _non_static_mode():
        indices = paddle.broadcast_to(indices, broadcast_shape)
        broadcast_shape_list = list(broadcast_shape)
        broadcast_shape_list[axis] = list(arr.shape)[axis]
        broadcast_shape = tuple(broadcast_shape_list)
        arr = paddle.broadcast_to(arr, broadcast_shape)
        if not _in_legacy_dygraph():
            return _C_ops.take_along_axis(arr, indices, axis)
        return _legacy_C_ops.take_along_axis(arr, indices, 'Axis', axis)
    check_variable_and_dtype(
        arr,
        'x',
        ['float16', 'float32', 'float64', 'int32', 'int64', 'uint8'],
        'take_along_axis',
    )
    check_variable_and_dtype(
        indices, 'index', ['int32', 'int64'], 'take_along_axis'
    )
    indices = paddle.broadcast_to(indices, broadcast_shape)
    broadcast_shape_list = list(broadcast_shape)
    broadcast_shape_list[axis] = list(arr.shape)[axis]
    broadcast_shape = tuple(broadcast_shape_list)
    arr = paddle.broadcast_to(arr, broadcast_shape)
    helper = LayerHelper('take_along_axis', **locals())
    dtype = helper.input_dtype()
    result = helper.create_variable_for_type_inference(dtype)
    helper.append_op(
        type="take_along_axis",
        inputs={"Input": arr, "Index": indices},
        attrs={"Axis": axis},
        outputs={"Result": result},
    )
    return result


def put_along_axis(arr, indices, values, axis, reduce='assign'):
    """
    Put values into the destination array by given indices matrix along the designated axis.

    Args:
        arr (Tensor) : The Destination Tensor. Supported data types are float32 and float64.
        indices (Tensor) : Indices to put along each 1d slice of arr. This must match the dimension of arr,
            and need to broadcast against arr. Supported data type are int and int64.
        axis (int) : The axis to put 1d slices along.
        reduce (str, optional): The reduce operation, default is 'assign', support 'add', 'assign', 'mul' and 'multiply'.

    Returns:
        Tensor, The indexed element, same dtype with arr

    Examples:
        .. code-block:: python

            import paddle

            x = paddle.to_tensor([[10, 30, 20], [60, 40, 50]])
            index = paddle.to_tensor([[0]])
            value = 99
            axis = 0
            result = paddle.put_along_axis(x, index, value, axis)
            print(result)
            # [[99, 99, 99],
            # [60, 40, 50]]

    """
    if len(arr.shape) != len(indices.shape):
        raise ValueError(
            "`indices` and `arr` must have the same number of dimensions!"
        )
    axis = non_negative_axis(arr, axis)
    broadcast_shape = infer_broadcast_shape(arr, indices, axis)
    if _non_static_mode():
        values = (
            paddle.to_tensor(values)
            if not isinstance(values, paddle.Tensor)
            else values
        )
        if broadcast_shape:
            indices = paddle.broadcast_to(indices, broadcast_shape)
        values = paddle.broadcast_to(values, indices.shape)
        if in_dygraph_mode():
            return _C_ops.put_along_axis(arr, indices, values, axis, reduce)
        return _legacy_C_ops.put_along_axis(
            arr, indices, values, "Axis", axis, "Reduce", reduce
        )

    check_variable_and_dtype(
        arr,
        'x',
        ['float16', 'float32', 'float64', 'int32', 'int64', 'uint8'],
        'put_along_axis',
    )
    check_variable_and_dtype(
        indices, 'index', ['int32', 'int64'], 'put_along_axis'
    )
    if broadcast_shape:
        indices = paddle.broadcast_to(indices, broadcast_shape)
    values = paddle.broadcast_to(values, indices.shape)
    helper = LayerHelper('put_along_axis', **locals())
    dtype = helper.input_dtype()
    result = helper.create_variable_for_type_inference(dtype)
    helper.append_op(
        type="put_along_axis",
        inputs={"Input": arr, "Index": indices, "Value": values},
        attrs={"Axis": axis, "Reduce": reduce},
        outputs={"Result": result},
    )
    return result


@inplace_apis_in_dygraph_only
def put_along_axis_(arr, indices, values, axis, reduce='assign'):
    r"""
    Inplace version of ``put_along_axis`` API, the output Tensor will be inplaced with input ``arr``.
    Please refer to :ref:`api_tensor_put_along_axis`.
    """
    if len(arr.shape) != len(indices.shape):
        raise ValueError(
            "`indices` and `arr` must have the same number of dimensions!"
        )
    axis = non_negative_axis(arr, axis)
    broadcast_shape = infer_broadcast_shape(arr, indices, axis)
    values = (
        paddle.to_tensor(values)
        if not isinstance(values, paddle.Tensor)
        else values
    )
    if broadcast_shape:
        indices = paddle.broadcast_to(indices, broadcast_shape)
    values = paddle.broadcast_to(values, indices.shape)
    if in_dygraph_mode():
        return _C_ops.put_along_axis_(arr, indices, values, axis, reduce)
    return _legacy_C_ops.put_along_axis_(
        arr, indices, values, "Axis", axis, "Reduce", reduce
    )


def index_add(x, index, axis, value, name=None):
    """
    Adds the elements of the input tensor with value tensor by selecting the indices in the order given in index.

    Args:
        x (Tensor) : The Destination Tensor. Supported data types are int32, int64, float16, float32, float64.
        index (Tensor): The 1-D Tensor containing the indices to index.
            The data type of ``index`` must be int32 or int64.
        axis (int): The dimension in which we index.
        value (Tensor): The tensor used to add the elements along the target axis.
        name(str, optional): For details, please refer to :ref:`api_guide_Name`. Generally, no setting is required. Default: None.

    Returns:
        Tensor, same dimention and dtype with x.

    Examples:
        .. code-block:: python

            # required: gpu
            import paddle

            input_tensor = paddle.to_tensor(paddle.ones((3, 3)), dtype="float32")
            index = paddle.to_tensor([0, 2], dtype="int32")
            value = paddle.to_tensor([[1, 1, 1], [1, 1, 1]], dtype="float32")
            outplace_res = paddle.index_add(input_tensor, index, 0, value)
            print(outplace_res)
            # Tensor(shape=[3, 3], dtype=float32, place=Place(gpu:0), stop_gradient=True,
            #        [[2., 2., 2.],
            #         [1., 1., 1.],
            #         [2., 2., 2.]])
    """
    if in_dygraph_mode():
        return _C_ops.index_add(x, index, value, axis)

    helper = LayerHelper("index_add", **locals())
    check_variable_and_dtype(
        x,
        'x',
        ['float16', 'float32', 'float64', 'int32', 'int64'],
        'paddle.tensor.manipulation.index_add',
    )
    check_variable_and_dtype(
        index,
        'index',
        ['int32', 'int64'],
        'paddle.tensor.manipulation.index_add',
    )
    check_variable_and_dtype(
        value,
        'add_value',
        ['float16', 'float32', 'float64', 'int32', 'int64'],
        'paddle.tensor.manipulation.index_add',
    )

    out = helper.create_variable_for_type_inference(x.dtype)

    helper.append_op(
        type='index_add',
        inputs={
            'X': x,
            'Index': index,
            'AddValue': value,
        },
        outputs={'Out': out},
        attrs={'axis': axis},
    )
    return out


@inplace_apis_in_dygraph_only
def index_add_(x, index, axis, value, name=None):
    """
    Inplace version of ``index_add`` API, the output Tensor will be inplaced with input ``x``.
    Please refer to :ref:`api_paddle_index_add`.

    Examples:
        .. code-block:: python

            # required: gpu
            import paddle

            input_tensor = paddle.to_tensor(paddle.ones((3, 3)), dtype="float32")
            index = paddle.to_tensor([0, 2], dtype="int32")
            value = paddle.to_tensor([[1, 1], [1, 1], [1, 1]], dtype="float32")
            inplace_res = paddle.index_add_(input_tensor, index, 1, value)
            print(inplace_res)
            # Tensor(shape=[3, 3], dtype=float32, place=Place(gpu:0), stop_gradient=True,
            #        [[2., 1., 2.],
            #         [2., 1., 2.],
            #         [2., 1., 2.]])
    """
    return _C_ops.index_add_(x, index, value, axis)


# TODO(dev): We need avoid implementing it by this way.
__METHODS = {
    'fill_': fill_,
    'zero_': zero_,
    'fill_diagonal_': fill_diagonal_,
    'fill_diagonal_tensor_': fill_diagonal_tensor_,
    "fill_diagonal_tensor": fill_diagonal_tensor,
    'tolist': tolist,
}
for name, func in __METHODS.items():
    setattr(core.VarBase, name, func)
    setattr(core.eager.Tensor, name, func)