code: def _docs_pooling():
      _docs.set_doc(
          chainerx.max_pool,
          """max_pool(x, ksize, stride=None, pad=0, cover_all=False)
  Spatial max pooling function.
  
  This acts similarly to :func:`~chainerx.conv`, but it computes the maximum
  of input spatial patch for each channel without any parameter instead of
  computing the inner products.
  
  Args:
      x (~chainerx.ndarray): Input array.
      ksize (int or tuple of ints): Size of pooling window. ``ksize=k`` and
          ``ksize=(k, k, ..., k)`` are equivalent.
      stride (int or tuple of ints or None): Stride of pooling applications.
          ``stride=s`` and ``stride=(s, s, ..., s)`` are equivalent. If
          ``None`` is specified, then it uses same stride as the pooling
          window size.
      pad (int or tuple of ints): Spatial padding width for the input array.
          ``pad=p`` and ``pad=(p, p, ..., p)`` are equivalent.
      cover_all (bool): If ``True``, all spatial locations are pooled into
          some output pixels. It may make the output size larger.
  
  Returns:
      :class:`~chainerx.ndarray`:  Output array.
  
  Note:
      During backpropagation, this function propagates the gradient of the
      output array to the input array ``x``. This function is only
      differentiable up to the second order.
  
  .. note::
     In ``cuda`` backend, only 2 and 3 dim arrays are supported as ``x``
     because cuDNN pooling supports 2 and 3 spatial dimensions.
  """,
      )
  
      _docs.set_doc(
          chainerx.average_pool,
          """average_pool(x, ksize, stride=None, pad=0, pad_mode='ignore')
  Spatial average pooling function.
  
  This acts similarly to :func:`~chainerx.conv`, but it computes the average
  of input spatial patch for each channel without any parameter instead of
  computing the inner products.
  
  Args:
      x (~chainerx.ndarray): Input array.
      ksize (int or tuple of ints): Size of pooling window. ``ksize=k`` and
          ``ksize=(k, k, ..., k)`` are equivalent.
      stride (int or tuple of ints or None): Stride of pooling applications.
          ``stride=s`` and ``stride=(s, s, ..., s)`` are equivalent. If
          ``None`` is specified, then it uses same stride as the pooling
          window size.
      pad (int or tuple of ints): Spatial padding width for the input array.
          ``pad=p`` and ``pad=(p, p, ..., p)`` are equivalent.
      pad_mode ({'zero', 'ignore'}): Specifies how padded region is treated.
  
          * 'zero' -- the values in the padded region are treated as 0
          * 'ignore' -- padded region is ignored (default)
  
  Returns:
      :class:`~chainerx.ndarray`:  Output array.
  
  Note:
      During backpropagation, this function propagates the gradient of the
      output array to the input array ``x``.
  
  .. note::
     In ``cuda`` backend, only 2 and 3 dim arrays are supported as ``x``
     because cuDNN pooling supports 2 and 3 spatial dimensions.
  """,
      )
  
file_path: chainer/chainerx/_docs/routines.py
imports: import chainerx
  from chainerx import _docs
label_desc: Use image node_id 14 for calling a global function with example usage: _docs_pooling() without return types