修复 paddle.nn.functional.one_hot等 API 的文档 (PaddlePaddle#58919)

* fix docs

* fix docs

python/paddle/nn/functional/input.py

@@ -25,12 +25,12 @@
 def one_hot(x, num_classes, name=None):
     """
 
-    The operator converts each id in the input 'x' to an one-hot vector with a
-    num_classes length. The value in the vector dimension corresponding to the id
+    The operator converts each id in the input `x` to an one-hot vector with a
+    `num_classes` length. The value in the vector dimension corresponding to the id
     is 1, and the value in the remaining dimension is 0.
 
-    The shape of output Tensor is generated by appending num_classes dimension
-    behind the last dimension of the 'x' shape.
+    The shape of output Tensor is generated by appending `num_classes` dimension
+    behind the last dimension of the `x` shape.
 
     .. code-block:: text
 
@@ -63,11 +63,14 @@ def one_hot(x, num_classes, name=None):
     Args:
         x(Tensor): Tensor with shape :math:`[N_1, N_2, ..., N_k]` ,
             which contains at least one dimension. The data type is int32 or int64.
-        num_classes(int): An integer defining the num_classes of the one hot dimension. If input 'x'
-            is word id, num_classes is generally the dictionary size.
+        num_classes(int): An integer defining the `num_classes` of the one hot dimension. If input `x`
+            is word id, `num_classes` is generally the dictionary size.
+        name(str|None, optional): For detailed information, please refer
+           to :ref:`api_guide_Name`. Usually name is no need to set and
+           None by default.
 
     Returns:
-        Tensor: The one-hot representations of 'x'. A Tensor with type float32.
+        Tensor: The one-hot representations of `x`. A Tensor with type float32.
 
     Examples:
         .. code-block:: python

python/paddle/tensor/manipulation.py

@@ -1591,9 +1591,7 @@ def flatten(x, start_axis=0, stop_axis=-1, name=None):
         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.
+        Tensor, A tensor with the contents of the input tensor, whose input axes are flattened by indicated :attr:`start_axis` and :attr:`end_axis`, and data type is the same as input :attr:`x`.
 
     Examples:
 

python/paddle/tensor/math.py

@@ -1055,6 +1055,9 @@ def multiply(x, y, name=None):
         out = x * y
 
     Note:
+        Supported shape of :attr:`x` and :attr:`y` for this operator:
+        1. `x.shape` == `y.shape`.
+        2. `x.shape` could be the continuous subsequence of `y.shape`.
         ``paddle.multiply`` supports broadcasting. If you would like to know more about broadcasting, please refer to `Introduction to Tensor`_ .
 
         .. _Introduction to Tensor: ../../guides/beginner/tensor_en.html#chapter5-broadcasting-of-tensor
@@ -1065,7 +1068,7 @@ def multiply(x, y, name=None):
         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 location into which the result is stored. If x, y have different shapes and are "broadcastable", the resulting tensor shape is the shape of x and y after broadcasting. If x, y have the same shape,  its shape is the same as x and y.
+        N-D Tensor. A location into which the result is stored. If :attr:`x`, :attr:`y` have different shapes and are "broadcastable", the resulting tensor shape is the shape of :attr:`x` and :attr:`y` after broadcasting. If :attr:`x`, :attr:`y` have the same shape, its shape is the same as :attr:`x` and :attr:`y`.
 
     Examples:
 

python/paddle/vision/transforms/transforms.py

@@ -315,12 +315,13 @@ class ToTensor(BaseTransform):
 
         .. code-block:: python
 
-            >>> import numpy as np
             >>> from PIL import Image
+            >>> import paddle
             >>> import paddle.vision.transforms as T
             >>> import paddle.vision.transforms.functional as F
 
-            >>> fake_img = Image.fromarray((np.random.rand(4, 5, 3) * 255.).astype(np.uint8))
+            >>> img_arr = ((paddle.rand((4, 5, 3)) * 255.).astype('uint8')).numpy()
+            >>> fake_img = Image.fromarray(img_arr)
             >>> transform = T.ToTensor()
             >>> tensor = transform(fake_img)
             >>> print(tensor.shape)