tensor.py

#   Copyright (c) 2018 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
#
# Unlessf 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.

from __future__ import print_function
import six
from six.moves import reduce
from ..layer_helper import LayerHelper
from ..param_attr import ParamAttr
from ..initializer import Initializer
from ..framework import convert_np_dtype_to_dtype_, in_dygraph_mode, _varbase_creator, device_guard, OpProtoHolder
from ..framework import Variable, in_dygraph_mode
from ..initializer import Constant
from ..core import VarDesc
from .. import core
from .layer_function_generator import templatedoc
from . import utils
from ..data_feeder import check_variable_and_dtype, check_type, check_dtype, convert_dtype
import numpy
import warnings

__all__ = [
    'create_tensor',
    'create_parameter',
    'create_global_var',
    'cast',
    'tensor_array_to_tensor',
    'concat',
    'sums',
    'assign',
    'fill_constant_batch_size_like',
    'fill_constant',
    'argmin',
    'argmax',
    'argsort',
    'ones',
    'zeros',
    'reverse',
    'has_inf',
    'has_nan',
    'isfinite',
    'range',
    'linspace',
    'full_like',
    'zeros_like',
    'ones_like',
    'diag',
    'eye',
    'kron',
    'arange',
    'full',
    'tril',
    'triu',
    'trace',
]


def create_tensor(dtype, name=None, persistable=False):
    """
    Create a variable, which will hold a Tensor with data type dtype.

    Args:
        dtype(string|numpy.dtype): the data type of Tensor to be created, the
            data type is bool, float16, float32, float64, int8, int16, int32 and int64.
        name(string, 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`
        persistable(bool): Set the persistable flag of the create tensor.
            default value is False.

    Returns:
        Variable: The tensor to be created according to dtype.

    Examples:
        .. code-block:: python

          import paddle.fluid as fluid
          tensor = fluid.layers.create_tensor(dtype='float32')
    """
    check_dtype(dtype, 'dtype', [
        'bool', 'float16', 'float32', 'float64', 'int8', 'int32', 'int32',
        'int64'
    ], 'create_tensor')
    helper = LayerHelper("create_tensor", **locals())
    return helper.create_variable(
        name=helper.name, dtype=dtype, persistable=persistable)


def create_parameter(shape,
                     dtype,
                     name=None,
                     attr=None,
                     is_bias=False,
                     default_initializer=None):
    """
    This function creates a parameter. The parameter is a learnable variable, which can have
    gradient, and can be optimized.

    NOTE: this is a very low-level API. This API is useful when you create
    operator by your self. instead of using layers.

    Parameters:
        shape (list of int): Shape of the parameter
        dtype (str): Data type of the parameter
        name (str, optional): For detailed information, please refer to
           :ref:`api_guide_Name` . Usually name is no need to set and None by default.
        attr (ParamAttr, optional): Attributes of the parameter
        is_bias (bool, optional): This can affect which default initializer is chosen
                       when default_initializer is None. If is_bias,
                       initializer.Constant(0.0) will be used. Otherwise,
                       Xavier() will be used.
        default_initializer (Initializer, optional): Initializer for the parameter

    Returns:
        The created parameter.

    Examples:
        .. code-block:: python

            import paddle.fluid as fluid
            import paddle.fluid.layers as layers
            W = layers.create_parameter(shape=[784, 200], dtype='float32')
    """
    check_type(shape, 'shape', (list, tuple, numpy.ndarray), 'create_parameter')
    for item in shape:
        if six.PY2:
            check_type(item, 'item of shape',
                       (int, long, numpy.uint8, numpy.int8, numpy.int16,
                        numpy.int32, numpy.int64), 'create_parameter')
        else:
            check_type(item, 'item of shape',
                       (int, numpy.uint8, numpy.int8, numpy.int16, numpy.int32,
                        numpy.int64), 'create_parameter')

    check_dtype(dtype, 'dtype', [
        'bool', 'float16', 'float32', 'float64', 'int8', 'int16', 'int32',
        'int64', 'uint8'
    ], 'create_parameter')
    check_type(attr, 'attr', (type(None), ParamAttr), 'create_parameter')
    check_type(default_initializer, 'default_initializer',
               (type(None), Initializer), 'create_parameter')

    helper = LayerHelper("create_parameter", **locals())
    if attr is None:
        attr = ParamAttr(name=name)
    return helper.create_parameter(attr, shape,
                                   convert_dtype(dtype), is_bias,
                                   default_initializer)


def create_global_var(shape,
                      value,
                      dtype,
                      persistable=False,
                      force_cpu=False,
                      name=None):
    """
    This function creates a new tensor variable with value in the global block(block 0).

    Parameters:
        shape (list of int): Shape of the variable
        value (float): The value of the variable. The new created
                      variable will be filled with it.
        dtype (str): Data type of the variable
        persistable (bool, optional): If this variable is persistable.
                           Default: False
        force_cpu (bool, optional): Force this variable to be on CPU.
                         Default: False
        name (str, optional): For detailed information, please refer to
           :ref:`api_guide_Name` . Usually name is no need to set and None by default.

    Returns:
        Variable: The created Variable

    Examples:
        .. code-block:: python

            import paddle.fluid as fluid
            import paddle.fluid.layers as layers
            var = layers.create_global_var(shape=[2,3], value=1.0, dtype='float32',
                                           persistable=True, force_cpu=True, name='new_var')
    """
    check_type(shape, 'shape', (list, tuple, numpy.ndarray),
               'create_global_var')
    for item in shape:
        if six.PY2:
            check_type(item, 'item of shape',
                       (int, long, numpy.uint8, numpy.int8, numpy.int16,
                        numpy.int32, numpy.int64), 'create_global_var')
        else:
            check_type(item, 'item of shape',
                       (int, numpy.uint8, numpy.int8, numpy.int16, numpy.int32,
                        numpy.int64), 'create_global_var')

    check_dtype(dtype, 'dtype', [
        'bool', 'float16', 'float32', 'float64', 'int8', 'int16', 'int32',
        'int64', 'uint8'
    ], 'create_global_var')

    helper = LayerHelper("global_var", **locals())
    var = helper.create_global_variable(
        dtype=dtype,
        shape=shape,
        persistable=persistable,
        name=name,
        stop_gradient=True)
    helper.set_variable_initializer(
        var, initializer=Constant(
            value=float(value), force_cpu=force_cpu))

    return var


def cast(x, dtype):
    """
    This OP takes in the Variable :attr:`x` with :attr:`x.dtype` and casts 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(Variable): An input N-D Tensor with data type bool, float16,
            float32, float64, int32, int64, uint8.
        dtype(np.dtype|core.VarDesc.VarType|str): Data type of the output:
            bool, float16, float32, float64, int8, int32, int64, uint8.

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

    Examples:
        .. code-block:: python

            import paddle.fluid as fluid
            import numpy as np

            place = fluid.core.CPUPlace()

            x_lod = fluid.data(name="x", shape=[2,2], lod_level=0)
            cast_res1 = fluid.layers.cast(x=x_lod, dtype="uint8")
            cast_res2 = fluid.layers.cast(x=x_lod, dtype=np.int32)

            exe = fluid.Executor(place)
            exe.run(fluid.default_startup_program())

            x_i_lod = fluid.core.LoDTensor()
            x_i_lod.set(np.array([[1.3,-2.4],[0,4]]).astype("float32"), place)
            x_i_lod.set_recursive_sequence_lengths([[0,2]])
            res1 = exe.run(fluid.default_main_program(), feed={'x':x_i_lod}, fetch_list=[cast_res1], return_numpy=False)
            res2 = exe.run(fluid.default_main_program(), feed={'x':x_i_lod}, fetch_list=[cast_res2], return_numpy=False)
            print(np.array(res1[0]), np.array(res1[0]).dtype)
            # [[  1 254]
            #  [  0   4]] uint8
            print(np.array(res2[0]), np.array(res2[0]).dtype)
            # [[ 1 -2]
            #  [ 0  4]] int32
    """
    check_variable_and_dtype(
        x, 'x',
        ['bool', 'float16', 'float32', 'float64', 'int32', 'int64', 'uint8'],
        'cast')
    check_dtype(dtype, 'dtype', [
        'bool', 'float16', 'float32', 'float64', 'int8', 'int32', 'int64',
        'uint8'
    ], 'cast')

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


def concat(input, axis=0, name=None):
    """
    **Concat**

    This OP concatenates the input along the axis.

    Args:
        input(list): List of input Tensors with data type float32, float64, int32,
            int64.
        axis(int32|Variable, optional):  A scalar with type ``int32`` or a ``Tensor`` with shape [1] and type ``int32``. Axis to compute indices along. 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): 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:
        Variable: A Tensor with the same data type as input's.

    Examples:
        .. code-block:: python

            import paddle.fluid as fluid
            import numpy as np

            in1 = np.array([[1,2,3],
                            [4,5,6]])
            in2 = np.array([[11,12,13],
                            [14,15,16]])
            in3 = np.array([[21,22],
                            [23,24]])
            with fluid.dygraph.guard():
                x1 = fluid.dygraph.to_variable(in1)
                x2 = fluid.dygraph.to_variable(in2)
                x3 = fluid.dygraph.to_variable(in3)
                out1 = fluid.layers.concat(input=[x1,x2,x3], axis=-1)
                out2 = fluid.layers.concat(input=[x1,x2], axis=0)
                print(out1.numpy())
                # [[ 1  2  3 11 12 13 21 22]
                #  [ 4  5  6 14 15 16 23 24]]
                print(out2.numpy())
                # [[ 1  2  3]
                #  [ 4  5  6]
                #  [11 12 13]
                #  [14 15 16]]
    """

    if in_dygraph_mode():
        if isinstance(axis, Variable):
            axis = axis.numpy()
            assert axis.shape == (
                1, ), "axis of type Variable should have shape [1]"
            axis = axis[0]
        return core.ops.concat(input, 'axis', axis)

    if not isinstance(input, list):
        warnings.warn(
            "The type of input in concat should be list, but received %s." %
            (type(input)))
        input = [input]
    for id, x in enumerate(input):
        check_variable_and_dtype(
            x, 'input[' + str(id) + ']',
            ['float16', 'float32', 'float64', 'int32', 'int64'], 'concat')
    check_type(axis, 'axis', (int, Variable), 'concat')

    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:
        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(x)
        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 tensor_array_to_tensor(input, axis=1, name=None, use_stack=False):
    """
    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(Variable): A LodTensorArray variable.
        axis(int): The axis along which the tensors in attr::`input` will be
            concatenated or stacked.
        name(str|None): A name for this layer(optional). If set None, the layer
                       will be named automatically.
        use_stack(bool): Act as concat_op or stack_op. For stack mode, all
            tensors in the tensor array must have the same shape.

    Returns:
        Variable: The concatenated or stacked tensor variable.
        Variable: 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 paddle.fluid as fluid
            import numpy as np
            x0 = fluid.layers.assign(np.random.rand(2, 2).astype("float32"))
            x1 = fluid.layers.assign(np.random.rand(2, 2).astype("float32"))
            i = fluid.layers.fill_constant(shape=[1], dtype="int64", value=0)
            array = fluid.layers.create_array(dtype='float32')
            fluid.layers.array_write(x0, i, array)
            fluid.layers.array_write(x1, i + 1, array)
            output, output_index = fluid.layers.tensor_array_to_tensor(input=array)
    """
    if in_dygraph_mode():
        assert isinstance(
            input, list), "The 'input' in tensor_array_to_tensor must be list"
        from .nn import stack, concat
        from ..dygraph import to_variable
        op = stack if use_stack else concat
        res = op(input, axis=axis)
        sizes = to_variable(
            numpy.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 sums(input, out=None):
    """
    This function computes the sum of multiple input Tensors elementwisely.

    - Case 1, sum of 3 Tensors

    .. code-block:: text

        # Input Tensors
        x0.shape = [2, 3]
        x0.data = [[1., 2., 3.],
                   [4., 5., 6.]]
        x1.shape = [2, 3]
        x1.data = [[10., 20., 30.],
                   [40., 50., 60.]]
        x2.shape = [2, 3]
        x2.data = [[100., 200., 300.],
                   [400., 500., 600.]]

        # Output Tensor
        out.shape = [2, 3]
        out.data = [[111., 222., 333.],
                    [444., 555., 666.]]

    Args:
        input (list): A list of Variables which hold input Tensors with the same
            data type and shape. Optional data types are: float32, float64, int32, int64.
        out (Variable, optional): Output Tensor. It can be any existing Variable.
            The default value is None, then a new Variable will be created and returned.

    Returns:
        Variable: The sum of inputs. The shape and data type is the same with input. \
            If :code:`out` is not None, the returned value is :code:`out` .

    Examples:
        .. code-block:: python

            import paddle.fluid as fluid

            x0 = fluid.layers.fill_constant(shape=[16, 32], dtype='int64', value=1)
            x1 = fluid.layers.fill_constant(shape=[16, 32], dtype='int64', value=2)
            x2 = fluid.layers.fill_constant(shape=[16, 32], dtype='int64', value=3)
            x3 = fluid.layers.fill_constant(shape=[16, 32], dtype='int64', value=0)

            # Sum of multiple Tensors, the result is stored to a new Variable sum0 (sum0=x0+x1+x2, the value is [[6, ..., 6], ..., [6, ..., 6]])
            sum0 = fluid.layers.sums(input=[x0, x1, x2])

            # Sum of multiple Tensors, sum1 and x3 represents the same Variable (x3=x0+x1+x2, the value is [[6, ..., 6], ..., [6, ..., 6]])
            sum1 = fluid.layers.sums(input=[x0, x1, x2], out=x3)
    """
    check_type(input, 'input', (Variable, tuple, list), 'sums')
    if isinstance(input, list) or isinstance(input, tuple):
        for input_section in input:
            check_variable_and_dtype(input_section, "input", \
                    ['float32', 'float64', 'int32', 'int64'], 'sums')
    else:
        check_variable_and_dtype(input, "input", \
                ['float32', 'float64', 'int32', 'int64'], 'sums')

    helper = LayerHelper('sum', **locals())
    if out is None:
        out = helper.create_variable_for_type_inference(
            dtype=helper.input_dtype())
    else:
        check_variable_and_dtype(
            out, "out", ['float32', 'float64', 'int32', 'int64'], 'sums')

    helper.append_op(
        type='sum',
        inputs={'X': input},
        outputs={'Out': out},
        attrs={'use_mkldnn': False})
    return out


def assign(input, output=None):
    """
    The OP copies the :attr:`input` to the :attr:`output`.

    Parameters:
        input (Variable|numpy.ndarray): A tensor or numpy ndarray, its data type supports
            float32, float64, int32 and int64.
        output (Variable, optional): A tensor. If :attr:`output` is None, a new tensor will
            be created as :attr:`output`. Default: None.

    Returns:
        Variable: A tensor with the same shape, data type and value as :attr:`input`.

    Examples:
        .. code-block:: python

          import paddle.fluid as fluid
          import numpy as np
          data = fluid.layers.fill_constant(shape=[3, 2], value=2.5, dtype='float64') # [[2.5, 2.5], [2.5, 2.5], [2.5, 2.5]]
          result1 = fluid.layers.create_tensor(dtype='float64')
          fluid.layers.assign(data, result1) # result1 = [[2.5, 2.5], [2.5, 2.5], [2.5, 2.5]]
          result2 = fluid.layers.assign(data)  # result2 = [[2.5, 2.5], [2.5, 2.5], [2.5, 2.5]]
          result3 = fluid.layers.assign(np.array([[2.5, 2.5], [2.5, 2.5], [2.5, 2.5]], dtype='float32')) # result3 = [[2.5, 2.5], [2.5, 2.5], [2.5, 2.5]]
    """
    helper = LayerHelper('assign', **locals())
    check_type(input, 'input', (Variable, numpy.ndarray), 'assign')
    if isinstance(input, Variable):
        check_dtype(input.dtype, 'input',
                    ['float32', 'float64', 'int32', 'int64', 'bool'], 'assign',
                    '(When the type of input in assign is Variable.)')
        if output is None:
            output = helper.create_variable_for_type_inference(
                dtype=input.dtype)
        helper.append_op(
            type='assign', inputs={'X': [input]}, outputs={'Out': [output]})
    elif isinstance(input, numpy.ndarray):
        dtype = convert_np_dtype_to_dtype_(input.dtype)
        if dtype == VarDesc.VarType.BOOL:
            value_name = "bool_values"
            values = [bool(v) for v in input.flat]
        elif dtype == VarDesc.VarType.FP32:
            value_name = "fp32_values"
            values = [float(v) for v in input.flat]
        elif dtype == VarDesc.VarType.INT32:
            value_name = "int32_values"
            values = [int(v) for v in input.flat]
        elif dtype == VarDesc.VarType.INT64:
            value_name = "int64_values"
            values = [int(v) for v in input.flat]
        else:
            raise TypeError(
                "When the type of 'input' in assign is numpy.ndarray, "
                "the data type of 'input' must be bool, float32, int32 or int64, but "
                "received %s." % convert_dtype(dtype))
        if input.size > 1024 * 1024:
            raise ValueError("The size of input is too big. Please consider "
                             "saving it to file and 'load_op' to load it")
        if output is None:
            output = helper.create_variable_for_type_inference(
                dtype=input.dtype)
        helper.append_op(
            type='assign_value',
            outputs={'Out': [output]},
            attrs={
                'dtype': dtype,
                'shape': list(input.shape),
                value_name: values
            })

    return output


def fill_constant(shape, dtype, value, force_cpu=False, out=None):
    """
    This OP creates a Tensor with specified `shape` and `dtype`, and
    initializes it with a constant specified by `value`.

    The attribute `stop_gradient` of the created Tensor is set to True.

    Args:
        shape(list|tuple|Variable): Shape of the Tensor to be created.
                The data type is ``int32`` or ``int64`` . If ``shape`` is a list or tuple,
                the elements of it should be integers or Tensors with shape [1].
                If ``shape`` is an Variable, it should be an 1-D Tensor .
        dtype(np.dtype|core.VarDesc.VarType|str): Data type of the output tensor which can
            be float16, float32, float64, int32, int64.
        value(float16|float32|float64|int32|int64|Variable): The constant value used to initialize 
            the Tensor to be created. If value is an Variable, it should be an 1-D Tensor.
        force_cpu(bool): data should be on CPU if it's true, default value is False.
        out(Variable, optional): Optional output which can be any created 
            Variable that meets the requirements to store the result of operation.
            if out is None, a new Varibale will be create to store the result.

    Returns:
        Variable: Tensor which is created according to shape and dtype.

    Raise:
        TypeError: The dtype must be one of bool, float16, float32, float64, int32 and int64
        and the data type of out Tensor must be the same as the dtype. 

    Examples:
        .. code-block:: python

          import paddle.fluid as fluid
          # attr shape is a list which doesn't contain Variable Tensor.
          data1 = fluid.layers.fill_constant(shape=[2,1], value=0, dtype='int64') # data1=[[0],[0]]
          data2 = fluid.layers.fill_constant(shape=[2,1], value=5, dtype='int64', out=data1)
          # data1=[[5], [5]] data2=[[5], [5]]

          # attr shape is a list which contains Variable Tensor.
          positive_2 = fluid.layers.fill_constant([1], "int32", 2)
          data3 = fluid.layers.fill_constant(shape=[1, positive_2], dtype='float32', value=1.5) # data3=[1.5, 1.5]

          # attr shape is an Variable Tensor.
          shape = fluid.layers.fill_constant([1,2], "int32", 2) # shape=[2,2]
          data4 = fluid.layers.fill_constant(shape=shape, dtype='bool', value=True) # data4=[[True,True],[True,True]]
          
          # attr value is an Variable Tensor.
          val = fluid.layers.fill_constant([1], "float32", 2.0) # val=[2.0]
          data5 = fluid.layers.fill_constant(shape=[2,1], value=val, dtype='float32') #data5=[[2.0],[2.0]]
    """
    inputs = {}
    attrs = {'force_cpu': force_cpu}
    if isinstance(value, Variable):
        inputs['ValueTensor'] = value
    else:
        attrs['value'] = float(value)
        if convert_dtype(dtype) in ['int64', 'int32']:
            attrs['str_value'] = str(int(value))
        else:
            attrs['str_value'] = str(float(value))

    if in_dygraph_mode():
        if isinstance(shape, (list, tuple)):
            shape = list(
                map(lambda x: x.numpy()[0] if isinstance(x, Variable) else x,
                    shape))
        else:
            shape = list(shape.numpy().astype(int))
        if out is None:
            out = _varbase_creator(dtype=dtype)

        if isinstance(value, Variable):
            if convert_dtype(dtype) in ['int64', 'int32']:
                attrs['str_value'] = str(int(value.numpy()))
            else:
                attrs['str_value'] = str(float(value.numpy()))

        core.ops.fill_constant(out, 'value',
                               float(value), 'force_cpu', force_cpu, 'dtype',
                               out.dtype, 'str_value', attrs['str_value'],
                               'shape', shape)
        out.stop_gradient = True
        return out

    check_dtype(dtype, 'dtype',
                ['bool', 'float16', 'float32', 'float64', 'int32', 'int64'],
                'fill_constant')
    check_type(shape, 'shape', (Variable, list, tuple), 'fill_constant')
    if isinstance(shape, Variable):
        check_variable_and_dtype(shape, 'shape', ['int32', 'int64'],
                                 'fill_constant')
    if out is not None:
        check_variable_and_dtype(out, 'out', [convert_dtype(dtype)],
                                 'fill_constant')

    helper = LayerHelper("fill_constant", **locals())
    inputs = utils._get_shape_tensor_inputs(
        inputs=inputs,
        helper=helper,
        attrs=attrs,
        shape=shape,
        op_type='fill_constant')

    if out is None:
        out = helper.create_variable_for_type_inference(dtype=dtype)
    attrs['dtype'] = out.dtype
    helper.append_op(
        type='fill_constant',
        inputs=inputs,
        outputs={'Out': [out]},
        attrs=attrs,
        stop_gradient=True)
    out.stop_gradient = True
    return out


@templatedoc()
def fill_constant_batch_size_like(input,
                                  shape,
                                  dtype,
                                  value,
                                  input_dim_idx=0,
                                  output_dim_idx=0,
                                  force_cpu=False):
    """
    This OP creates a Tesnor according the shape and dtype, and initializes the
    Tensor with the constants provided in ``value``. When the input is LoDTensor
    and the input_dim_idx is 0, the output_dim_idx dimension is set to the value
    of the batch_size input by the input, the Stop_gradient attribute of the created
    Tensor is False by default.

    Args:
        input(Variable): Tensor which data type is float32, float64, int32 and int64.
        shape(list): The shape of Tensor to be created, Tensor's shape may be changed
            according the input.
        dtype(np.dtype|core.VarDesc.VarType|str): The data type of created Tensor which
            can be float32, float64, int32, int64.
        value(float|int): The constant value used to initialize the Tensor to be created. 
        input_dim_idx(int): When the value is 0 and the input is LoDTensor, the output_dim_idx
            dimension of the created Tensor is set to the batch_size value of input.
            The default value is 0.
        output_dim_idx(int): Used to specify which dimension of Tensor is created to be set
            the value of batch_size of input Tensor. The default value is 0.
        force_cpu(bool): data should be on CPU if it's true, default value is False.

    Returns:
        Variable: Tensor which will be created according to dtype.

    Examples:

        .. code-block:: python

             import paddle.fluid as fluid
             like = fluid.layers.fill_constant(shape=[1,2], value=10, dtype='int64') #like=[[10, 10]]
             data = fluid.layers.fill_constant_batch_size_like(
                    input=like, shape=[1], value=0, dtype='int64') #like=[[10, 10]] data=[0]

    """
    helper = LayerHelper("fill_constant_batch_size_like", **locals())
    out = helper.create_variable_for_type_inference(dtype=dtype)
    attrs = {
        'shape': shape,
        'dtype': out.dtype,
        'value': float(value),
        'input_dim_idx': input_dim_idx,
        'output_dim_idx': output_dim_idx,
        'force_cpu': force_cpu
    }
    if convert_dtype(dtype) in ['int64', 'int32']:
        attrs['str_value'] = str(int(value))
    else:
        attrs['str_value'] = str(float(value))
    helper.append_op(
        type='fill_constant_batch_size_like',
        inputs={'Input': input},
        outputs={'Out': [out]},
        attrs=attrs)
    out.stop_gradient = True
    return out


def argmin(x, axis=0):
    """
    **argmin**

    This OP computes the indices of the min elements of the input tensor's
    element along the provided axis.

    Args:
        x(Variable): An input N-D Tensor with type float32, float64, int16,
            int32, int64, uint8.
        axis(int, optional): Axis to compute indices along. 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.

    Returns:
        Variable: A Tensor with data type int64.

    Examples:
        .. code-block:: python

            import paddle.fluid as fluid
            import numpy as np

            in1 = np.array([[[5,8,9,5],
                            [0,0,1,7],
                            [6,9,2,4]],
                            [[5,2,4,2],
                            [4,7,7,9],
                            [1,7,0,6]]])
            with fluid.dygraph.guard():
                x = fluid.dygraph.to_variable(in1)
                out1 = fluid.layers.argmin(x=x, axis=-1)
                out2 = fluid.layers.argmin(x=x, axis=0)
                out3 = fluid.layers.argmin(x=x, axis=1)
                out4 = fluid.layers.argmin(x=x, axis=2)
                print(out1.numpy())
                # [[0 0 2]
                #  [1 0 2]]
                print(out2.numpy())
                # [[0 1 1 1]
                #  [0 0 0 0]
                #  [1 1 1 0]]
                print(out3.numpy())
                # [[1 1 1 2]
                #  [2 0 2 0]]
                print(out4.numpy())
                # [[0 0 2]
                #  [1 0 2]]
    """
    check_variable_and_dtype(
        x, 'x', ['float32', 'float64', 'uint8', 'int16', 'int32', 'int64'],
        'argmin')
    helper = LayerHelper("arg_min", **locals())
    out = helper.create_variable_for_type_inference(VarDesc.VarType.INT64)
    helper.append_op(
        type='arg_min',
        inputs={'X': x},
        outputs={'Out': [out]},
        attrs={'axis': axis})
    out.stop_gradient = True
    return out


def argmax(x, axis=0):
    """
    **argmax**

    This OP computes the indices of the max elements of the input tensor's
    element along the provided axis.

    Args:
        x(Variable): An input N-D Tensor with type float32, float64, int16,
            int32, int64, uint8.
        axis(int, optional): Axis to compute indices along. 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.

    Returns:
        Variable: A Tensor with data type int64.

    Examples:
        .. code-block:: python

            import paddle.fluid as fluid
            import numpy as np

            in1 = np.array([[[5,8,9,5],
                            [0,0,1,7],
                            [6,9,2,4]],
                            [[5,2,4,2],
                            [4,7,7,9],
                            [1,7,0,6]]])
            with fluid.dygraph.guard():
                x = fluid.dygraph.to_variable(in1)
                out1 = fluid.layers.argmax(x=x, axis=-1)
                out2 = fluid.layers.argmax(x=x, axis=0)
                out3 = fluid.layers.argmax(x=x, axis=1)
                out4 = fluid.layers.argmax(x=x, axis=2)
                print(out1.numpy())
                # [[2 3 1]
                #  [0 3 1]]
                print(out2.numpy())
                # [[0 0 0 0]
                #  [1 1 1 1]
                #  [0 0 0 1]]
                print(out3.numpy())
                # [[2 2 0 1]
                #  [0 1 1 1]]
                print(out4.numpy())
                # [[2 3 1]
                #  [0 3 1]]
    """
    check_variable_and_dtype(
        x, 'x', ['float32', 'float64', 'uint8', 'int16', 'int32', 'int64'],
        'argmax')
    helper = LayerHelper("arg_max", **locals())
    out = helper.create_variable_for_type_inference(VarDesc.VarType.INT64)
    helper.append_op(
        type='arg_max',
        inputs={'X': x},
        outputs={'Out': [out]},
        attrs={'axis': axis})
    out.stop_gradient = True
    return out


def argsort(input, axis=-1, descending=False, name=None):
    """
    This OP sorts the input along the given axis, and returns sorted output
    data Varibale and its corresponding index Variable with the same shape as
    :attr:`input`.

    Args:
        input(Variable): An input N-D Tensor with type float32, float64, int16,
            int32, int64, uint8.
        axis(int, optional): Axis to compute indices along. 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.
        descending(bool, optional) : Descending is a flag, if set to true,
            algorithm will sort by descending order, else sort by
            ascending order. Default is false.
        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:
        tuple: A tuple of sorted data Variable(with the same shape and data
        type as input) and the sorted indices(with the same shape as input's
        and with data type int64).

    Examples:
        .. code-block:: python

            import paddle.fluid as fluid
            import numpy as np

            in1 = np.array([[[5,8,9,5],
                            [0,0,1,7],
                            [6,9,2,4]],
                            [[5,2,4,2],
                            [4,7,7,9],
                            [1,7,0,6]]]).astype(np.float32)
            with fluid.dygraph.guard():
                x = fluid.dygraph.to_variable(in1)
                out1 = fluid.layers.argsort(input=x, axis=-1)
                out2 = fluid.layers.argsort(input=x, axis=0)
                out3 = fluid.layers.argsort(input=x, axis=1)
                print(out1[0].numpy())
                # [[[5. 5. 8. 9.]
                #   [0. 0. 1. 7.]
                #   [2. 4. 6. 9.]]
                #  [[2. 2. 4. 5.]
                #   [4. 7. 7. 9.]
                #   [0. 1. 6. 7.]]]
                print(out1[1].numpy())
                # [[[0 3 1 2]
                #   [0 1 2 3]
                #   [2 3 0 1]]
                #  [[1 3 2 0]
                #   [0 1 2 3]
                #   [2 0 3 1]]]
                print(out2[0].numpy())
                # [[[5. 2. 4. 2.]
                #   [0. 0. 1. 7.]
                #   [1. 7. 0. 4.]]
                #  [[5. 8. 9. 5.]
                #   [4. 7. 7. 9.]
                #   [6. 9. 2. 6.]]]
                print(out3[0].numpy())
                # [[[0. 0. 1. 4.]
                #   [5. 8. 2. 5.]
                #   [6. 9. 9. 7.]]
                #  [[1. 2. 0. 2.]
                #   [4. 7. 4. 6.]
                #   [5. 7. 7. 9.]]]
    """
    check_variable_and_dtype(
        input, 'input',
        ['float32', 'float64', 'int16', 'int32', 'int64', 'uint8'], 'argsort')
    helper = LayerHelper("argsort", **locals())
    out = helper.create_variable_for_type_inference(
        dtype=input.dtype, stop_gradient=True)
    ids = helper.create_variable_for_type_inference(
        VarDesc.VarType.INT64, stop_gradient=True)
    helper.append_op(
        type='argsort',
        inputs={'X': input},
        outputs={'Out': out,
                 'Indices': ids},
        attrs={'axis': axis,
               'descending': descending})
    return out, ids


def ones(shape, dtype, force_cpu=False):
    """
    The OP creates a tensor of specified :attr:`shape` and :attr:`dtype`, and fills it with 1.
    Its :attr:`stop_gradient` will be set to True to stop gradient computation.

    Parameters:
        shape (tuple|list): Shape of output tensor.
        dtype (np.dtype|core.VarDesc.VarType|str): Data type of output tensor, it supports
            bool, float16, float32, float64, int32 and int64.
        force_cpu (bool, optional): Whether force to store the output tensor in CPU memory.
            If :attr:`force_cpu` is False, the output tensor will be stored in running device memory.
            Default: False.

    Returns:
        Variable: A tensor of data type :attr:`dtype` with shape :attr:`shape` and all elements set to 1.

    Examples:
        .. code-block:: python

          import paddle.fluid as fluid
          data = fluid.layers.ones(shape=[2, 4], dtype='float32') # [[1., 1., 1., 1.], [1., 1., 1., 1.]]
    """
    check_type(shape, 'shape', (list, tuple), 'ones')
    check_dtype(dtype, 'create data type',
                ['bool', 'float16', 'float32', 'float64', 'int32', 'int64'],
                'ones')
    assert reduce(lambda x, y: x * y,
                  shape) > 0, "The shape is invalid: %s." % (str(shape))
    return fill_constant(value=1.0, **locals())


def zeros(shape, dtype, force_cpu=False):
    """
    The OP creates a tensor of specified :attr:`shape` and :attr:`dtype`, and fills it with 0.
    Its :attr:`stop_gradient` will be set to True to stop gradient computation.

    Parameters:
        shape (tuple|list): Shape of output tensor.
        dtype (np.dtype|core.VarDesc.VarType|str): Data type of output tensor, it supports
            bool, float16, float32, float64, int32 and int64.
        force_cpu (bool, optional): Whether force to store the output tensor in CPU memory.
            If :attr:`force_cpu` is False, the output tensor will be stored in running device memory.
            Default: False.

    Returns:
        Variable: A tensor of data type :attr:`dtype` with shape :attr:`shape` and all elements set to 0.

    Examples:
        .. code-block:: python

          import paddle.fluid as fluid
          data = fluid.layers.zeros(shape=[3, 2], dtype='float32') # [[0., 0.], [0., 0.], [0., 0.]]
    """
    check_type(shape, 'shape', (list, tuple), 'zeros')
    check_dtype(dtype, 'create data type',
                ['bool', 'float16', 'float32', 'float64', 'int32', 'int64'],
                'zeros')
    return fill_constant(value=0.0, **locals())


def reverse(x, axis):
    """
    The OP reverses the tensor :attr:`x` along the given :attr:`axis`.

    Parameters:
        x (Variable): A tensor to be reversed, its data type supports bool, float32, float64, int32, int64 and uint8.
        axis (int|tuple|list): A dimension or a set of dimensions of :attr:`x` to reverse. Must be
            in the range [-rank( :attr:`x` ), rank( :attr:`x` )). If it is a tuple or a list, reversing
            will be apply on each axis in the tuple or list.

    Returns:
        Variable: The reversed tensor with the same shape and data type as :attr:`x`.

    Examples:
        .. code-block:: python

          import paddle.fluid as fluid
          import numpy as np
          data = fluid.layers.assign(np.array([[0, 1, 2], [3, 4, 5], [6, 7, 8]], dtype='float32')) # [[0., 1., 2.], [3., 4., 5.], [6., 7., 8.]]
          result1 = fluid.layers.reverse(data, 0) # [[6., 7., 8.], [3., 4., 5.], [0., 1., 2.]]
          result2 = fluid.layers.reverse(data, [0, 1]) # [[8., 7., 6.], [5., 4., 3.], [2., 1., 0.]]
    """
    if isinstance(axis, int):
        axis = [axis]
    helper = LayerHelper("reverse", **locals())
    out = helper.create_variable_for_type_inference(dtype=x.dtype)
    helper.append_op(
        type='reverse',
        inputs={'X': x},
        outputs={'Out': [out]},
        attrs={'axis': axis})
    return out


def save(x, file_path, overwrite=True):
    """
    Saves a variable as a file.

    Args:
        x(variable): The Tensor/LoDTensor to be saved.
        file_path(str): The file path where the variable will be saved.
        overwrite(bool): Whether or not cover the given file when it has already
            existed. If it's set 'False' and the file is existed, a runtime
            error will be thrown.
    """
    helper = LayerHelper("save", **locals())
    helper.append_op(
        type="save",
        inputs={"input": x},
        outputs={},
        args={"file_path": file_path,
              "overwrite": overwrite})


def save_combine(x, file_path, overwrite=True):
    """
    Saves a list of variables into a single file.

    Args:
        x(list): A list of Tensor/LoDTensor variables to be saved together in
                 a single file.
        file_path(str): The file path where variables will be saved.
        overwrite(bool): Whether or not cover the given file when it has already
            existed. If it's set 'False' and the file is existed, a runtime
            error will be thrown.

    Returns:
        There is no return value.

    Examples:

        .. code-block:: python

            import paddle.fluid as fluid
            v1 = fluid.layers.data(name="data",
                                   shape=(4, 6),
                                   dtype="float32")
            v2 = fluid.layers.data(name="data",
                                   shape=(6, 8, 4),
                                   dtype="float32")
            normed = fluid.layers.save_combine([v1, v2], file_path="output")
    """
    helper = LayerHelper("save_combine", **locals())
    helper.append_op(
        type="save_combine",
        inputs={"input": x},
        outputs={},
        args={"file_path": file_path,
              "overwrite": overwrite})


def load_combine(out, file_path):
    """
    Loads a list of variable from a single file.

    Args:
        out(list): The list of variables to be read from the disk file.
        file_path(str): The path of the disk file.
    """
    helper = LayerHelper("load_combine", **locals())
    helper.append_op(
        type="load_combine",
        inputs={},
        output={"Out": out},
        args={"file_path": file_path})


def has_inf(x):
    """
    Test if any of x contains an infinity number

    Args:
       x (Variable): The Tensor/LoDTensor to be checked.

    Returns:
       Variable: The tensor variable storing the output, only a bool value, indicating that whether there is infinity number in x or not.
    
    Examples:
        .. code-block:: python
          
          import paddle.fluid as fluid
          data = fluid.layers.data(name="input", shape=[4, 32, 32], dtype="float32")
          res = fluid.layers.has_inf(data)

    """
    check_type(x, 'x', (Variable), 'has_inf')
    helper = LayerHelper("isinf", **locals())
    out = helper.create_variable_for_type_inference(dtype=x.dtype)
    helper.append_op(type="isinf", inputs={"X": x}, outputs={"Out": out})
    return out


def has_nan(x):
    """
    Test if any of x contains a NAN

    Args:
       x (Variable): The Tensor/LoDTensor to be checked.

    Returns:
       Variable: The tensor variable storing the output, only a bool value, indicating that whether there is NAN in x or not.
    
    Examples:
        .. code-block:: python
    
          import paddle.fluid as fluid
          data = fluid.layers.data(name="input", shape=[4, 32, 32], dtype="float32")
          res = fluid.layers.has_nan(data)

    """
    check_type(x, 'x', (Variable), 'has_nan')
    helper = LayerHelper("isnan", **locals())
    out = helper.create_variable_for_type_inference(dtype=x.dtype)
    helper.append_op(type="isnan", inputs={"X": x}, outputs={"Out": out})
    return out


def isfinite(x):
    """
    Test if any of x contains an infinity/NAN number. If all the elements are finite,
    returns true, else false.

    Args:
       x(variable): The Tensor/LoDTensor to be checked.

    Returns:
        Variable: The tensor variable storing the output, contains a bool value.

    Examples:

        .. code-block:: python

            import paddle.fluid as fluid
            var = fluid.layers.data(name="data",
                                    shape=(4, 6),
                                    dtype="float32")
            out = fluid.layers.isfinite(var)
    """
    check_variable_and_dtype(x, "x", ["float32", "float64", "int32", "int64"],
                             "isfinite")
    helper = LayerHelper("isfinite", **locals())

    out = helper.create_variable_for_type_inference(dtype='bool')
    helper.append_op(type="isfinite", inputs={"X": x}, outputs={"Out": out})
    return out


def range(start, end, step, dtype):
    """
    Return evenly spaced values within a given interval.

    Values are generated within the half-open interval [start, stop) (in other words,
    the interval including start but excluding stop).

    Parameters:
        start(float32 | float64 | int32 | int64 | Variable): Start of interval. The interval includes this value.
            when start is Variable, it is a 1-D Tensor with shape [1].
        end(float32 | float64 | int32 | int64 | Variable): End of interval. The interval does not include this
                                 value, except in some cases where step is not an integer
                                 and floating point round-off affects the length of out. When end is Variable,
                                 it is a 1-D Tensor with shape [1].
        step(float32 | float64 | int32 | int64 | Variable): Spacing between values. For any output out, this is the
                                  distance between two adjacent values, out[i+1] - out[i].
        dtype(str|core.VarDesc.VarType): the data type of the output tensor, can be float32, float64, int32, int64.

    Returns: a 1-D Tensor which is evenly spaced values within a given interval. Its data type is set by dtype.
    
    Return type: Variable

    examples:

        .. code-block:: python

             import paddle.fluid as fluid
             data = fluid.layers.range(0, 10, 2, 'int32')

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

    check_dtype(dtype, 'create data type',
                ['float32', 'float64', 'int32', 'int64'], 'range')

    dtype = convert_dtype(dtype)
    if not isinstance(start, Variable):
        start = fill_constant([1], dtype, start)
    elif convert_dtype(start.dtype) != dtype:
        # make sure that start, end, step has the same dtype as
        # `dtype`
        start = cast(x=start, dtype=dtype)

    if not isinstance(end, Variable):
        end = fill_constant([1], dtype, end)
    elif convert_dtype(end.dtype) != dtype:
        end = cast(x=end, dtype=dtype)

    if not isinstance(step, Variable):
        step = fill_constant([1], dtype, step)
    elif convert_dtype(step.dtype) != dtype:
        step = cast(x=step, dtype=dtype)

    out = helper.create_variable_for_type_inference(dtype=start.dtype)

    helper.append_op(
        type='range',
        inputs={'Start': start,
                'End': end,
                'Step': step},
        outputs={'Out': [out]})
    out.stop_gradient = True
    return out


def linspace(start, stop, num, dtype):
    """
    This OP return fixed number of evenly spaced values within a given interval.

    Args:
        start(float|Variable): The input :attr:`start` is start variable of range. It is a float scalar, \
            or a tensor of shape [1] with input data type float32, float64.
        stop(float|Variable): The input :attr:`stop` is start variable of range. It is a float scalar, \
            or a tensor of shape [1] with input data type float32, float64.
        num(int|Variable): The input :attr:`num` is given num of the sequence. It is an int scalar, \
            or a tensor of shape [1] with type int32.
        dtype(string): The data type of output tensor, it could be 'float32' and 'float64'.

    Returns:
        Variable, the output data type will be float32, float64.: The 1-D tensor with fixed number of evenly spaced values, \
        the data shape of this tensor is :math:`[num]` . If the :attr:`num` is set 1, the output tensor just has \
        the value with input :attr:`start`. 

    Examples:
        .. code-block:: python

             import paddle.fluid as fluid
             data = fluid.layers.linspace(0, 10, 5, 'float32') # [0.0,  2.5,  5.0,  7.5, 10.0]
             data = fluid.layers.linspace(0, 10, 1, 'float32') # [0.0]

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

    check_type(start, 'start', (Variable, float, int), linspace)
    check_type(stop, 'stop', (Variable, float, int), linspace)
    check_type(num, 'num', (Variable, float, int), linspace)

    if not isinstance(start, Variable):
        start = fill_constant([1], dtype, start)
    else:
        check_variable_and_dtype(start, "start", ["float32", "float64"],
                                 "linspace")

    if not isinstance(stop, Variable):
        stop = fill_constant([1], dtype, stop)
    else:
        check_variable_and_dtype(stop, "stop", ["float32", "float64"],
                                 "linspace")
    if not isinstance(num, Variable):
        num = fill_constant([1], 'int32', num)
    else:
        check_variable_and_dtype(num, "num", ["int32"], "linspace")

    out = helper.create_variable_for_type_inference(dtype=start.dtype)

    helper.append_op(
        type='linspace',
        inputs={'Start': start,
                'Stop': stop,
                'Num': num},
        outputs={'Out': [out]})
    return out


def full_like(input,
              fill_value,
              out=None,
              dtype=None,
              device=None,
              stop_gradient=True,
              name=None):
    """
    **full_like**
    This function creates a tensor filled with `fill_value` which has identical shape and dtype 
    with `input`.

    Args:
        input(Variable): The input tensor which specifies shape and data type. The data type can be bool, float16, float32, float64, int32, int64.
        fill_value(bool|float|int): The value to fill the tensor with. Default value is 0. Note: this value shouldn't exceed the range of the output data type.
        out(Variable, optional): Optional output which can be any created Variable that meets the requirements to store the result of operation. If out is None, a new Varibale will be create to store the result. Default value is None.
        dtype(np.dtype|core.VarDesc.VarType|str, optional): The data type of output. The default value is None, which means the output data type is the same as input.
        device (string, optional): Which device to run the operator. The :attr:`device` must be None, 'cpu', 'gpu'. If :attr:`device` is None, it will be the device that the user set in the paddle program. Default value is None.
        stop_gradient(bool, optional): Indicating if we stop gradient from current(out) Variable. 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:
        out(Variable): The Tensor variable storing the output.
    
    Examples:
        .. code-block:: python

          import paddle
          import paddle.fluid as fluid
          import numpy as np
          input = fluid.data(name='input', dtype='float32', shape=[2, 3])
          output = fluid.layers.full_like(input, 2.0)
          exe = fluid.Executor(fluid.CPUPlace())
          exe.run(fluid.default_startup_program())
          img=np.array([[1, 2, 3], [4, 5, 6]]).astype(np.float32)
          res = exe.run(fluid.default_main_program(), feed={'input':img}, fetch_list=[output])
          print(res) # [array([[2., 2., 2.], [2., 2., 2.]], dtype=float32)]
    """
    helper = LayerHelper("full_like", **locals())

    var_dtype = None
    if dtype is None:
        var_dtype = input.dtype
    else:
        check_dtype(
            dtype, 'dtype',
            ['bool', 'float16', 'float32', 'float64', 'int32', 'int64'],
            'full_like')
        var_dtype = convert_np_dtype_to_dtype_(dtype)

    if out is None:
        out = helper.create_variable_for_type_inference(dtype=dtype)

    helper.append_op(
        type='fill_any_like',
        inputs={'X': [input]},
        attrs={'value': fill_value,
               "dtype": var_dtype},
        outputs={'Out': [out]})
    out.stop_gradient = stop_gradient

    return out


def zeros_like(x, out=None):
    """
    This OP creates a zeros tensor which has identical shape and dtype 
    with `x`.

    Args:
        x(Variable): The input tensor which specifies shape and dtype, the input data dtype could be bool, float32, float64, int32, int64.
        out(Variable, optional): If is :attr:`None` , the op will create the variable as output, the data type and shape of \
            this variable will be same as input :attr:`x`. If is a tensor, the data type and shape need to be same as input :attr:`x`. 
            The default value is :attr:`None` .

    Returns:
        Variable: The N-D tensor, the element in tensor is related to input data type, if the input data type is bool, \
            the output value is False, otherwise is zero. The output shape is the same as the input.

    Examples:
        .. code-block:: python

          import paddle.fluid as fluid
          x = fluid.data(name='x', dtype='float32', shape=[3])
          data = fluid.layers.zeros_like(x) # [0.0, 0.0, 0.0]

    """

    check_variable_and_dtype(
        x, "x", ['bool', 'float32', 'float64', 'int32', 'int64'], 'ones_like')
    helper = LayerHelper("zeros_like", **locals())
    if out is None:
        out = helper.create_variable_for_type_inference(dtype=x.dtype)
    else:
        check_variable_and_dtype(
            out, "out", ['bool', 'float32', 'float64', 'int32', 'int64'],
            'ones_like')

    helper.append_op(
        type='fill_zeros_like', inputs={'X': [x]}, outputs={'Out': [out]})
    out.stop_gradient = True
    return out


def diag(diagonal):
    """
    This OP creates a square matrix which has diagonal values specified by input :attr:`diagonal`.

    Args:
        diagonal(Variable|numpy.ndarray): The input tensor should be 1D tensor, the input shape is :math:`[ N]` , \
            specifying diagonal values by this input tensor. The input data type should be float32, float64, int32, int64.

    Returns:
        Variable, the output data type is the same as input data type.: The tensor variable storing the square matrix, \
            the diagonal values specified by input :attr:`diagonal`. the output shape is :math:`[N, N]` with two dims.

    Examples:
        .. code-block:: python

          # [[3, 0, 0]
          #  [0, 4, 0]
          #  [0, 0, 5] 

          import paddle.fluid as fluid
          import numpy as np
          diagonal = np.arange(3, 6, dtype='int32')
          data = fluid.layers.diag(diagonal)
          # diagonal.shape=(3,) data.shape=(3, 3)

    """
    check_type(diagonal, 'diagonal', (Variable, numpy.ndarray), 'diag')
    check_dtype(diagonal.dtype, 'diagonal',
                ['float32', 'float64', 'int32', 'int64'], 'diag')
    helper = LayerHelper("diag", **locals())

    if not isinstance(diagonal, Variable):
        diagonal = assign(diagonal)

    out = helper.create_variable_for_type_inference(dtype=diagonal.dtype)

    helper.append_op(
        type='diag', inputs={'Diagonal': [diagonal]}, outputs={'Out': [out]})

    out.stop_gradient = True
    return out


def eye(num_rows, num_columns=None, batch_shape=None, dtype='float32'):
    """
    **eye**

    This function constructs an identity tensor, or a batch of tensor.

    Args:
        num_rows(int): the number of rows in each batch tensor.
        num_columns(int): the number of columns in each batch tensor.
                          If None, default: num_rows.
        batch_shape(list(int)): If provided, the returned tensor will have a leading
                                batch size of this shape.
        dtype(string): The data type of the returned tensor.
                       It should be int32, int64, float16, float32, float64.

    Returns:
        Variable: An identity Tensor or LoDTensor of shape batch_shape + [num_rows, num_columns].

    Examples:
        .. code-block:: python

          import paddle.fluid as fluid
          data = fluid.layers.eye(3, dtype='int32')
          # [[1, 0, 0]
          #  [0, 1, 0]
          #  [0, 0, 1]]

          data = fluid.layers.eye(2, 3, dtype='int32')
          # [[1, 0, 0]
          #  [0, 1, 0]]

          data = fluid.layers.eye(2, batch_shape=[3])
          # Construct a batch of 3 identity tensors, each 2 x 2.
          # data[i, :, :] is a 2 x 2 identity tensor, i = 0, 1, 2.

    """

    helper = LayerHelper("eye", **locals())
    if not isinstance(num_rows, int) or num_rows < 0:
        raise TypeError("num_rows should be a non-negative int")
    if num_columns is not None:
        if not isinstance(num_columns, int) or num_columns < 0:
            raise TypeError("num_columns should be a non-negative int")
    else:
        num_columns = num_rows
    out = helper.create_variable_for_type_inference(dtype=dtype)
    c_dtype = convert_np_dtype_to_dtype_(dtype)
    helper.append_op(
        type='eye',
        inputs={},
        outputs={'Out': [out]},
        attrs={
            'num_rows': num_rows,
            'num_columns': num_columns,
            'dtype': c_dtype
        },
        stop_gradient=True)
    out.stop_gradient = True

    if batch_shape is not None:
        if not isinstance(batch_shape, list):
            raise TypeError("batch_shape should be a list")
        from .nn import stack
        for batch_val in reversed(batch_shape):
            if batch_val <= 0:
                raise TypeError("batch_shape should be a positive int list")
            else:
                stack_vars = [out for _ in numpy.arange(batch_val)]
                out = stack(stack_vars, axis=0)
    return out


def ones_like(x, out=None):
    """
    **ones_like**

    This function creates a ones tensor which has identical shape and dtype 
    with `x`.

    Args:
        x(Variable): The input tensor which specifies shape and dtype.
        out(Variable): The output tensor.

    Returns:
        out(Variable): The tensor variable storing the output.

    Examples:
        .. code-block:: python

          import paddle.fluid as fluid

          x = fluid.layers.data(name='x', dtype='float32', shape=[3], append_batch_size=False)
          data = fluid.layers.ones_like(x) # [1.0, 1.0, 1.0]

    """
    check_variable_and_dtype(
        x, "x", ['bool', 'float32', 'float64', 'int32', 'int64'], 'ones_like')

    helper = LayerHelper("ones_like", **locals())
    if out is None:
        out = helper.create_variable_for_type_inference(dtype=x.dtype)
    else:
        check_variable_and_dtype(
            out, "out", ['bool', 'float32', 'float64', 'int32', 'int64'],
            'ones_like')
    helper.append_op(
        type='fill_any_like',
        inputs={'X': [x]},
        attrs={'value': 1.0},
        outputs={'Out': [out]})
    return out


def arange(start, end, step=1, dtype=None, name=None):
    """
    Return evenly spaced values within a given interval.
    Values are generated within the half-open interval [start, stop) (in other words,
    the interval including start but excluding stop).
    Parameters:
        start(float32 | float64 | int32 | int64 | Variable): Start of interval. The interval includes this value.
            when start is Variable, it is a 1-D Tensor with shape [1].
        end(float32 | float64 | int32 | int64 | Variable): End of interval. The interval does not include this
                                 value, except in some cases where step is not an integer
                                 and floating point round-off affects the length of out. When end is Variable,
                                 it is a 1-D Tensor with shape [1].
        step(float32 | float64 | int32 | int64 | Variable): Spacing between values. For any output out, this is the
                                  distance between two adjacent values, out[i+1] - out[i].
        dtype(str|core.VarDesc.VarType): the data type of the output tensor, can be float32, float64, int32, int64.
    Returns: a 1-D Tensor which is evenly spaced values within a given interval. Its data type is set by dtype.
    
    Return type: Variable
    examples:
        .. code-block:: python
             import paddle.fluid as fluid
             # expected out put: [0, 2, 4, 6, 8]
             data = fluid.layers.arange(0, 10, 2, 'int32')
         #dygraph mode
             import paddle.fluid as fluid
             with fluid.dygraph.guard():
                 x = fluid.layers.arange(0, 6, 2) 
                 # x: [0, 2, 4]
                 # x dtype: float32
             
    """
    helper = LayerHelper("range", **locals())

    if dtype is None:
        dtype = 'float32'

    check_dtype(dtype, 'create data type',
                ['float32', 'float64', 'int32', 'int64'], 'range')

    dtype = convert_dtype(dtype)
    if not isinstance(start, Variable):
        start = fill_constant([1], dtype, start)

    if not isinstance(end, Variable):
        end = fill_constant([1], dtype, end)

    if not isinstance(step, Variable):
        step = fill_constant([1], dtype, step)

    out = helper.create_variable_for_type_inference(dtype=start.dtype)

    helper.append_op(
        type='range',
        inputs={'Start': start,
                'End': end,
                'Step': step},
        outputs={'Out': [out]})
    out.stop_gradient = True
    return out


def full(shape,
         fill_value,
         out=None,
         dtype=None,
         device=None,
         stop_gradient=True,
         name=None):
    """
    This Op return a Tensor with the `fill_value` which size is same as `shape`
    
    Args:
        shape(list|tuple|Variable): Shape of the Tensor to be created.
                The data type is ``int32`` or ``int64`` . If ``shape`` is a list or tuple,
                the elements of it should be integers or Tensors with shape [1].
                If ``shape`` is an Variable, it should be an 1-D Tensor .
        fill_value(bool|float16|float32|float64|int32|int64|Variable): The constant value
            used to initialize the Tensor to be created. If fill_value is an Variable, it must be an 1-D Tensor.
        out(Variable, optional): Optional output which can be any created 
            Variable that meets the requirements to store the result of operation.
            if out is None, a new Varibale will be create to store the result.
        dtype(np.dtype|core.VarDesc.VarType|str, optional): Data type of the output tensor
            which can be float16, float32, float64, int32, int64, if dytpe is `None`, the data
            type of created tensor is `float32`
        device(str, optional): On which device to run this Op. The :attr:`device` must be
            None, 'cpu' or 'gpu'. If :attr:`device` is None, the device that the user set in 
            the paddle program will be chosen. Default value is None.
        stop_gradient(bool, optional): Indicating if we stop gradient from current(out) Variable,
            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:
        Variable: Tensor which is created according to shape and dtype.

    Raises:
        TypeError: The `dtype` must be one of None, bool, float16, float32, float64, int32 and int64.
        TypeError: The `out` must be a Variable.
        TypeError: The `shape` must be one of Variable, list tuple.
    
    Examples:
        .. code-block:: python

          import paddle.fluid as fluid

          data1 = fluid.layers.full(shape=[2,1], fill_value=0, dtype='int64') # data1=[[0],[0]]
          data2 = fluid.layers.full(shape=[2,1], fill_value=5, dtype='int64', device='gpu') # data2=[[5],[5]]

          # attr shape is a list which contains Variable Tensor.
          positive_2 = fluid.layers.fill_constant([1], "int32", 2)
          data3 = fluid.layers.full(shape=[1, positive_2], dtype='float32', fill_value=1.5) # data3=[1.5, 1.5]

          # attr shape is an Variable Tensor.
          shape = fluid.layers.fill_constant([1,2], "int32", 2) # shape=[2,2]
          data4 = fluid.layers.full(shape=shape, dtype='bool', fill_value=True) # data4=[[True,True],[True,True]]
          
          # attr value is an Variable Tensor.
          val = fluid.layers.fill_constant([1], "float32", 2.0) # val=[2.0]
          data5 = fluid.layers.full(shape=[2,1], fill_value=val, dtype='float32') #data5=[[2.0],[2.0]]
    """

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

    if dtype is None:
        dtype = 'float32'

    check_dtype(dtype, 'create data type',
                ['bool', 'float16', 'float32', 'float64', 'int32', 'int64'],
                'full')
    check_type(shape, 'shape', (Variable, list, tuple), 'full')
    if out is not None:
        check_type(shape, 'out', (Variable), 'full')

    if out is None:
        out = helper.create_variable_for_type_inference(dtype=dtype)

    out.stop_gradient = stop_gradient

    with device_guard(device):
        out = fill_constant(shape=shape, dtype=dtype, value=fill_value, out=out)

    return out


def _tril_triu_op(helper):
    """Base op of tril_op and triu_op
    """
    op_type = helper.layer_type
    x = helper.kwargs.get('input', None)

    assert x is not None, 'x cannot be None in {}'.format(op_type)
    check_variable_and_dtype(x, 'x', ['float32', 'float64', 'int32', 'int64'],
                             op_type)
    if len(x.shape) < 2:
        raise ValueError("input shape in {} must be at least 2-D".format(
            op_type))
    diagonal = helper.kwargs.get('diagonal', 0)
    if not isinstance(diagonal, (int, )):
        raise TypeError("diagonal in {} must be a python Int".format(op_type))
    name = helper.kwargs.get('name', None)

    if name is None:
        out = helper.create_variable_for_type_inference(dtype=x.dtype)
    else:
        out = helper.create_variable(
            name=name, dtype=x.dtype, persistable=False)

    helper.append_op(
        type="tril_triu",
        inputs={"X": x},
        attrs={
            "diagonal": diagonal,
            "lower": True if op_type == 'tril' else False,
        },
        outputs={"Out": out}, )

    return out


def tril(input, diagonal=0, name=None):
    """
    This op returns the lower triangular part of a matrix (2-D tensor) or batch
    of matrices :attr:`input`, the other elements of the result tensor are set 
    to 0. The lower triangular part of the matrix is defined as the elements 
    on and below the diagonal.

    Args:
        input (Variable): The input variable which is a Tensor.
            Support data types: ``float64``, ``float32``, ``int32``, ``int64``.
        diagonal (int, optional): The diagonal to consider, default value is 0.
            If :attr:`diagonal` = 0, all elements on and below the main diagonal are
            retained. A positive value includes just as many diagonals above the main
            diagonal, and similarly a negative value excludes just as many diagonals below
            the main diagonal. The main diagonal are the set of indices
            :math:`\{(i, i)\}` for :math:`i \in [0, \min\{d_{1}, d_{2}\} - 1]` where
            :math:`d_{1}, d_{2}` are the dimensions of the matrix.
        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:
        Variable: Tensor, results of lower triangular operation by the specified diagonal of input tensor,
        it's data type is the same as input's Tensor.

    Raises:
        TypeError: diagonal is not a int type.
        ValueError: dimension of :attr:`input` is less than 2.

    Examples:
        .. code-block:: python

            import numpy as np
            import paddle.fluid as fluid

            data = np.arange(1, 13, dtype="int64").reshape(3,-1)
            # array([[ 1,  2,  3,  4],
            #        [ 5,  6,  7,  8],
            #        [ 9, 10, 11, 12]])
            x = fluid.data(shape=(-1, 4), dtype='int64', name='x')
            exe = fluid.Executor(fluid.CPUPlace())

            # example 1, default diagonal
            tril = fluid.layers.tril(x)
            tril_out, = exe.run(fluid.default_main_program(), feed={"x": data},
                fetch_list=[tril], return_numpy=True)
            # array([[ 1,  0,  0,  0],
            #        [ 5,  6,  0,  0],
            #        [ 9, 10, 11,  0]])

        .. code-block:: python

            # example 2, positive diagonal value
            import paddle.fluid as fluid
            import numpy as np

            data = np.arange(1, 13, dtype="int64").reshape(3,-1)
            x = fluid.data(shape=(-1, 4), dtype='int64', name='x')
            exe = fluid.Executor(fluid.CPUPlace())

            tril = fluid.layers.tril(x, diagonal=2)
            tril_out, = exe.run(fluid.default_main_program(), feed={"x": data},
                fetch_list=[tril], return_numpy=True)
            # array([[ 1,  2,  3,  0], 
            #        [ 5,  6,  7,  8],
            #        [ 9, 10, 11, 12]])

        .. code-block:: python

            # example 3, negative diagonal value
            import paddle.fluid as fluid
            import numpy as np

            data = np.arange(1, 13, dtype="int64").reshape(3,-1)
            x = fluid.data(shape=(-1, 4), dtype='int64', name='x')
            exe = fluid.Executor(fluid.CPUPlace())

            tril = fluid.layers.tril(x, diagonal=-1)
            tril_out, = exe.run(fluid.default_main_program(), feed={"x": data},
                fetch_list=[tril], return_numpy=True)
            # array([[ 0,  0,  0,  0],
            #        [ 5,  0,  0,  0],
            #        [ 9, 10,  0,  0]])

   """

    return _tril_triu_op(LayerHelper('tril', **locals()))


def triu(input, diagonal=0, name=None):
    """
    This op returns the upper triangular part of a matrix (2-D tensor) or batch of matrices
    :attr:`input`, the other elements of the result tensor are set to 0.
    The upper triangular part of the matrix is defined as the elements on and
    above the diagonal.

    Args:
        input (Variable): The input variable which is a Tensor.
            Support data types: ``float64``, ``float32``, ``int32``, ``int64``.
        diagonal (int, optional): The diagonal to consider, default value is 0.
            If :attr:`diagonal` = 0, all elements on and above the main diagonal are
            retained. A positive value excludes just as many diagonals above the main
            diagonal, and similarly a negative value includes just as many diagonals below
            the main diagonal. The main diagonal are the set of indices
            :math:`\{(i, i)\}` for :math:`i \in [0, \min\{d_{1}, d_{2}\} - 1]` where
            :math:`d_{1}, d_{2}` are the dimensions of the matrix.
        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:
        Variable: Tensor, results of upper triangular operation by the specified diagonal of input tensor,
        it's data type is the same as input's Tensor.

    Raises:
        TypeError: diagonal is not a int type.
        ValueError: dimension of :attr:`input` is less than 2.

    Examples:
        .. code-block:: python

            import numpy as np
            import paddle.fluid as fluid

            data = np.arange(1, 13, dtype="int64").reshape(3,-1)
            # array([[ 1,  2,  3,  4],
            #        [ 5,  6,  7,  8],
            #        [ 9, 10, 11, 12]])
            x = fluid.data(shape=(-1, 4), dtype='int64', name='x')
            exe = fluid.Executor(fluid.CPUPlace())

            # example 1, default diagonal
            import paddle.fluid as fluid
            triu = fluid.layers.triu(x)
            triu_out, = exe.run(fluid.default_main_program(), feed={"x": data},
                fetch_list=[triu], return_numpy=True)
            # array([[ 1,  2,  3,  4],
            #        [ 0,  6,  7,  8],
            #        [ 0,  0, 11, 12]])

        .. code-block:: python

            # example 2, positive diagonal value
            import paddle.fluid as fluid
            import numpy as np

            data = np.arange(1, 13, dtype="int64").reshape(3,-1)
            x = fluid.data(shape=(-1, 4), dtype='int64', name='x')
            exe = fluid.Executor(fluid.CPUPlace())

            triu = fluid.layers.triu(x, diagonal=2)
            triu_out, = exe.run(fluid.default_main_program(), feed={"x": data},
                fetch_list=[triu], return_numpy=True)
            # array([[0, 0, 3, 4],
            #        [0, 0, 0, 8],
            #        [0, 0, 0, 0]])

        .. code-block:: python

            # example 3, negative diagonal value
            import paddle.fluid as fluid
            import numpy as np

            data = np.arange(1, 13, dtype="int64").reshape(3,-1)
            x = fluid.data(shape=(-1, 4), dtype='int64', name='x')
            exe = fluid.Executor(fluid.CPUPlace())

            triu = fluid.layers.triu(x, diagonal=-1)
            triu_out, = exe.run(fluid.default_main_program(), feed={"x": data},
                fetch_list=[triu], return_numpy=True)
            # array([[ 1,  2,  3,  4],
            #        [ 5,  6,  7,  8],
            #        [ 0, 10, 11, 12]])

    """

    return _tril_triu_op(LayerHelper('triu', **locals()))


@templatedoc(op_type="kron")
def kron(x, y, out=None, name=None):
    """${comment}

    Args:
        x (Variable): the fist operand of kron op, data type: float16, float32, 
            float64, int32 or int64.
        y (Variable): the second operand of kron op, data type: float16, 
            float32, float64, int32 or int64. Its data type should be the same 
            with x.
        out (Variable, optional): Optional output which can be any created 
            Variable that meets the requirements to store the result of 
            operation. If out is None, a new Varibale will be create to store 
            the result. Defaults to 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:
        Variable: The output of kron op, data type: float16, float32, float64, int32 or int64. Its data is the same with x.

    Examples:
        .. code-block:: python
        
          import paddle
          from paddle import fluid
          import paddle.fluid.dygraph as dg
          import numpy as np

          a = np.arange(1, 5).reshape(2, 2).astype(np.float32)
          b = np.arange(1, 10).reshape(3, 3).astype(np.float32)

          place = fluid.CPUPlace()
          with dg.guard(place):
              a_var = dg.to_variable(a)
              b_var = dg.to_variable(b)
              c_var = fluid.layers.kron(a_var, b_var)
              c_np = c_var.numpy()
          print(c_np)

          #[[ 1.  2.  3.  2.  4.  6.]
          # [ 4.  5.  6.  8. 10. 12.]
          # [ 7.  8.  9. 14. 16. 18.]
          # [ 3.  6.  9.  4.  8. 12.]
          # [12. 15. 18. 16. 20. 24.]
          # [21. 24. 27. 28. 32. 36.]]
    """
    if in_dygraph_mode():
        return core.ops.kron(x, y)

    helper = LayerHelper('kron', **locals())
    check_variable_and_dtype(
        x, 'x', ['float16', 'float32', 'float64', 'int32', 'int64'], 'kron')
    check_variable_and_dtype(
        y, 'y', ['float16', 'float32', 'float64', 'int32', 'int64'], 'kron')

    if out is None:
        out = helper.create_variable_for_type_inference(dtype=x.dtype)
    else:
        check_variable_and_dtype(
            out, 'out', ['float16', 'float32', 'float64', 'int32', 'int64'],
            'kron')
    helper.append_op(type="kron", inputs={"X": x, "Y": y}, outputs={"Out": out})
    return out


def trace(input, offset=0, dim1=0, dim2=1, out=None, name=None):
    """
    This OP computes the sum along diagonals of the input tensor.
    
    If ``input`` is 2D, returns the sum of diagonal. 

    If ``input`` has larger dimensions, then returns an tensor of diagonals sum, diagonals be taken from
    the 2D planes specified by dim1 and dim2. By default, the 2D planes formed by the first and second dimensions 
    of the input tensor.

    The argument ``offset`` determines where diagonals are taken from input tensor:

    - If offset = 0, it is the main diagonal.
    - If offset > 0, it is above the main diagonal.
    - If offset < 0, it is below the main diagonal.
    
    Args:
        input(Variable): The input tensor. Must be at least 2-dimensional. The input data type should be float32, float64, int32, int64.
        offset(int, optional): Which diagonals in input tensor will be taken. Default: 0 (main diagonals).
        dim1(int, optional): The first dimension with respect to take diagonal. Default: 0.
        dim2(int, optional): The second dimension with respect to take diagonal. Default: 1.
        name (str, optional): Normally there is no need for user to set this property. For more information, please refer to :ref:`api_guide_Name`. Default: None.

    Returns:
        Variable: the output data type is the same as input data type.

    Examples:
        .. code-block:: python

            import paddle.fluid as fluid
            import paddle.fluid.dygraph as dg
            import numpy as np
            
            case1 = np.random.randn(2, 3).astype('float32')
            case2 = np.random.randn(3, 10, 10).astype('float32')
            case3 = np.random.randn(3, 10, 5, 10).astype('float32')
            
            with dg.guard():
                case1 = dg.to_variable(case1)
                case2 = dg.to_variable(case2)
                case3 = dg.to_variable(case3)
                data1 = fluid.layers.trace(case1) # data1.shape = [1]
                data2 = fluid.layers.trace(case2, offset=1, dim1=1, dim2=2) # data2.shape = [3]
                data3 = fluid.layers.trace(case3, offset=-3, dim1=1, dim2=-1) # data2.shape = [3, 5]
    """
    inputs = {'Input': [input]}
    attrs = {'offset': offset, 'dim1': dim1, 'dim2': dim2}

    def __check_input(input, offset, dim1, dim2):
        check_dtype(input.dtype, 'Input',
                    ['int32', 'int64', 'float16', 'float32', 'float64'],
                    'trace')

        input_shape = list(input.shape)
        assert len(input_shape) >= 2,                     \
                "The input must be at least 2-dimensional, "   \
                "But received Input's dimensional: %s.\n" %  \
                len(input_shape)

        dim1_ = dim1 if dim1 >= 0 else len(input_shape) + dim1
        dim2_ = dim2 if dim2 >= 0 else len(input_shape) + dim2

        assert dim1_ < len(input_shape),     \
            "The argument dim1 is out of range (expected to be in range of [%d, %d], but got %d).\n"  \
            % (-(len(input_shape)), len(input_shape) - 1, dim1)

        assert dim2_ < len(input_shape),   \
            "The argument dim2 is out of range (expected to be in range of [%d, %d], but got %d).\n"   \
            % (-(len(input_shape)), len(input_shape) - 1, dim2)


        assert  dim1_ != dim2_,   \
               "dim1 and dim2 cannot be the same dimension." \
                "But received dim1 = %d, dim2 = %d\n"%(dim1, dim2)

    if not in_dygraph_mode():
        __check_input(input, offset, dim1, dim2)
    helper = LayerHelper('trace', **locals())

    if out is None:
        out = helper.create_variable_for_type_inference(dtype=input.dtype)
    else:
        check_variable_and_dtype(
            out, 'out', ['float16', 'float32', 'float64', 'int32', 'int64'],
            'trace')

    helper.append_op(
        type='trace',
        inputs={'Input': [input]},
        attrs={'offset': offset,
               'dim1': dim1,
               'dim2': dim2},
        outputs={'Out': [out]})
    return out