未验证 提交 085b9619 编写于 作者: 李灿 提交者: GitHub

fix bugs test=develop (#28125)

上级 5d700021
...@@ -1100,7 +1100,7 @@ def max(x, axis=None, keepdim=False, name=None): ...@@ -1100,7 +1100,7 @@ def max(x, axis=None, keepdim=False, name=None):
float64, int32, int64. float64, int32, int64.
axis(list|int, optional): The axis along which the maximum is computed. axis(list|int, optional): The axis along which the maximum is computed.
If :attr:`None`, compute the maximum over all elements of If :attr:`None`, compute the maximum over all elements of
`x` and return a Tensor variable with a single element, `x` and return a Tensor variable with a single element,
otherwise must be in the range :math:`[-x.ndim(x), x.ndim(x))`. otherwise must be in the range :math:`[-x.ndim(x), x.ndim(x))`.
If :math:`axis[i] < 0`, the axis to reduce is :math:`x.ndim + axis[i]`. If :math:`axis[i] < 0`, the axis to reduce is :math:`x.ndim + axis[i]`.
keepdim(bool, optional): Whether to reserve the reduced dimension in the keepdim(bool, optional): Whether to reserve the reduced dimension in the
......
...@@ -380,7 +380,9 @@ def uniform(shape, dtype=None, min=-1.0, max=1.0, seed=0, name=None): ...@@ -380,7 +380,9 @@ def uniform(shape, dtype=None, min=-1.0, max=1.0, seed=0, name=None):
distribution in the range [``min``, ``max``), with ``shape`` and ``dtype``. distribution in the range [``min``, ``max``), with ``shape`` and ``dtype``.
Examples: Examples:
:: ::
Input: Input:
shape = [1, 2] shape = [1, 2]
Output: Output:
......
...@@ -62,7 +62,9 @@ def argsort(x, axis=-1, descending=False, name=None): ...@@ -62,7 +62,9 @@ def argsort(x, axis=-1, descending=False, name=None):
and with data type int64). and with data type int64).
Examples: Examples:
.. code-block:: python .. code-block:: python
import paddle import paddle
paddle.disable_static() paddle.disable_static()
...@@ -358,6 +360,7 @@ def nonzero(x, as_tuple=False): ...@@ -358,6 +360,7 @@ def nonzero(x, as_tuple=False):
.. code-block:: python .. code-block:: python
import paddle import paddle
x1 = paddle.to_tensor([[1.0, 0.0, 0.0], x1 = paddle.to_tensor([[1.0, 0.0, 0.0],
......
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