docs(mge): update MaxPool2d & functional.nn.max_pool2d docstring

GitOrigin-RevId: b46f227b59ef98d16e1c08a2ae3526467a79cd60

imperative/python/megengine/functional/nn.py

@@ -658,16 +658,16 @@ def max_pool2d(
     Refer to :class:`~.MaxPool2d` for more information.
 
     Args:
-        inp: input tensor of shape :math:`(N, C, H_{in}, W_{in})`.
+        inp: input tensor of shape :math:`(N, C, H_{\text{in}}, W_{\text{in}})`.
         kernel_size: size of the window used to calculate the max value.
-        stride: stride of the window. If not provided, its value is set to kernel_size.
-            Default: ``None``
-        padding: implicit zero padding added on both sides. Default: :math:`0`
+        stride: stride of the window. Default value is ``kernel_size``.
+        padding: implicit zero padding added on both sides. Default: 0.
 
     Returns:
-        output tensor of shape `(N, C, H_{out}, W_{out})`.
+        output tensor of shape `(N, C, H_{\text{out}}, W_{\text{out}})`.
 
     Examples:
+        >>> import numpy as np
         >>> input = tensor(np.arange(1 * 1 * 3 * 4).astype(np.float32).reshape(1, 1, 3, 4))
         >>> F.nn.max_pool2d(input, 2, 1, 0)
         Tensor([[[[ 5.  6.  7.]

imperative/python/megengine/module/pooling.py

@@ -32,9 +32,9 @@ class _PoolNd(Module):
 class MaxPool2d(_PoolNd):
     r"""Applies a 2D max pooling over an input.
 
-    For instance, given an input of the size :math:`(N, C, H, W)` and
+    For instance, given an input of the size :`(N, C, H_{\text{in}}, W_{\text{in}})` and
     :attr:`kernel_size` :math:`(kH, kW)`, this layer generates the output of
-    the size :math:`(N, C, H_{out}, W_{out})` through a process described as:
+    the size :math:`(N, C, H_{\text{out}}, W_{\text{out}})` through a process described as:
 
     .. math::
 
@@ -48,9 +48,9 @@ class MaxPool2d(_PoolNd):
     both sides for :attr:`padding` number of points.
 
     Args:
-        kernel_size: the size of the window to take a max over.
-        stride: the stride of the window. Default value is kernel_size.
-        padding: implicit zero padding to be added on both sides.
+        kernel_size: the size of the window.
+        stride: the stride of the window. Default value is ``kernel_size``.
+        padding: implicit zero padding to be added on both sides.Default: 0.
 
     Examples:
         >>> import numpy as np