修复paddle.nn.functinal包和paddle.nn包下API文档 (#48581)

Author: Fancy-hjyp

python/paddle/nn/functional/conv.py

@@ -614,10 +614,10 @@ def conv2d(
             the number of output channels, g is the number of groups, kH is the filter's
             height, kW is the filter's width.
         bias (Tensor, optional): The bias with shape [M,].
-        stride (int|list|tuple): The stride size. It means the stride in convolution.
+        stride (int|list|tuple, optional): The stride size. It means the stride in convolution.
             If stride is a list/tuple, it must contain two integers, (stride_height, stride_width).
             Otherwise, stride_height = stride_width = stride. Default: stride = 1.
-        padding (string|int|list|tuple): The padding size. It means the number of zero-paddings
+        padding (string|int|list|tuple, optional): The padding size. It means the number of zero-paddings
             on both sides for each dimension.If `padding` is a string, either 'VALID' or
             'SAME' which is the padding algorithm. If padding size is a tuple or list,
             it could be in three forms: `[pad_height, pad_width]` or
@@ -627,11 +627,11 @@ def conv2d(
             when `data_format` is `"NHWC"`, `padding` can be in the form
             `[[0,0], [pad_height_top, pad_height_bottom], [pad_width_left, pad_width_right], [0,0]]`.
             Default: padding = 0.
-        dilation (int|list|tuple): The dilation size. It means the spacing between the kernel
+        dilation (int|list|tuple, optional): The dilation size. It means the spacing between the kernel
             points. If dilation is a list/tuple, it must contain two integers, (dilation_height,
             dilation_width). Otherwise, dilation_height = dilation_width = dilation.
             Default: dilation = 1.
-        groups (int): The groups number of the Conv2D Layer. According to grouped
+        groups (int, optional): The groups number of the Conv2D Layer. According to grouped
             convolution in Alex Krizhevsky's Deep CNN paper: when group=2,
             the first half of the filters is only connected to the first half
             of the input channels, while the second half of the filters is only

python/paddle/nn/layer/conv.py

@@ -449,7 +449,7 @@ class Conv1DTranspose(_ConvNd):
         in_channels(int): The number of channels in the input image.
         out_channels(int): The number of the filter. It is as same as the output
             feature map.
-        kernel_size(int|tuple|list, optional): The filter size. If kernel_size is a tuple/list,
+        kernel_size(int|tuple|list): The filter size. If kernel_size is a tuple/list,
             it must contain one integers, (kernel_size). None if
             use output size to calculate kernel_size. Default: None. kernel_size and
             output_size should not be None at the same time.
@@ -598,7 +598,7 @@ class Conv2D(_ConvNd):
     Parameters:
         in_channels(int): The number of input channels in the input image.
         out_channels(int): The number of output channels produced by the convolution.
-        kernel_size(int|list|tuple, optional): The size of the convolving kernel.
+        kernel_size(int|list|tuple): The size of the convolving kernel.
         stride(int|list|tuple, optional): The stride size. If stride is a list/tuple, it must
             contain three integers, (stride_H, stride_W). Otherwise, the
             stride_H = stride_W = stride. The default value is 1.
@@ -925,7 +925,7 @@ class Conv3D(_ConvNd):
     Parameters:
         in_channels(int): The number of input channels in the input image.
         out_channels(int): The number of output channels produced by the convolution.
-        kernel_size(int|list|tuple, optional): The size of the convolving kernel.
+        kernel_size(int|list|tuple): The size of the convolving kernel.
         stride(int|list|tuple, optional): The stride size. If stride is a list/tuple, it must
             contain three integers, (stride_D, stride_H, stride_W). Otherwise, the
             stride_D = stride_H = stride_W = stride. The default value is 1.