parameters: (operand, pooling_type, pooling_window_shape, strides=(1,), auto_padding=[False],
              ceil_out_dim=False, include_pad=False, name='')
return_statement: return pooling(operand, pooling_type, pooling_window_shape, strides, auto_padding,
                     ceil_out_dim, include_pad, name)
docstring: The pooling operations compute a new tensor by selecting the maximum or average value in the pooling input.
      In the case of average pooling with padding, the average is only over the valid region.
  
      N-dimensional pooling allows to create max or average pooling of any dimensions, stride or padding.
  
      Example:
          >>> img = np.reshape(np.arange(16, dtype = np.float32), [1, 4, 4])
          >>> x = C.input_variable(img.shape)
          >>> C.pooling(x, C.AVG_POOLING, (2,2), (2,2)).eval({x : [img]})
          array([[[[  2.5,   4.5],
                    [ 10.5,  12.5]]]], dtype=float32)
          >>> C.pooling(x, C.MAX_POOLING, (2,2), (2,2)).eval({x : [img]})
          array([[[[  5.,   7.],
                    [ 13.,  15.]]]], dtype=float32)
  
      Args:
          operand: pooling input
          pooling_type: one of :const:`~cntk.ops.MAX_POOLING` or :const:`~cntk.ops.AVG_POOLING`
          pooling_window_shape: dimensions of the pooling window
          strides (default 1): strides.
          auto_padding (default [False,]): automatic padding flags for each input dimension.
          ceil_out_dim (default False): ceiling while computing output size
          include_pad(default False): include pad while average pooling
          name (str, optional): the name of the Function instance in the network
      Returns:
          :class:`~cntk.ops.functions.Function`
docstring_summary: The pooling operations compute a new tensor by selecting the maximum or average value in the pooling input.
      In the case of average pooling with padding, the average is only over the valid region.
docstring_tokens: The pooling operations compute a new tensor by selecting the maximum or average value in the pooling input . In the case of average pooling with padding the average is only over the valid region .
function: def pooling(operand, pooling_type, pooling_window_shape, strides=(1,), auto_padding=[False],
              ceil_out_dim=False, include_pad=False, name=''):
      '''
      The pooling operations compute a new tensor by selecting the maximum or average value in the pooling input.
      In the case of average pooling with padding, the average is only over the valid region.
  
      N-dimensional pooling allows to create max or average pooling of any dimensions, stride or padding.
  
      Example:
          >>> img = np.reshape(np.arange(16, dtype = np.float32), [1, 4, 4])
          >>> x = C.input_variable(img.shape)
          >>> C.pooling(x, C.AVG_POOLING, (2,2), (2,2)).eval({x : [img]})
          array([[[[  2.5,   4.5],
                    [ 10.5,  12.5]]]], dtype=float32)
          >>> C.pooling(x, C.MAX_POOLING, (2,2), (2,2)).eval({x : [img]})
          array([[[[  5.,   7.],
                    [ 13.,  15.]]]], dtype=float32)
  
      Args:
          operand: pooling input
          pooling_type: one of :const:`~cntk.ops.MAX_POOLING` or :const:`~cntk.ops.AVG_POOLING`
          pooling_window_shape: dimensions of the pooling window
          strides (default 1): strides.
          auto_padding (default [False,]): automatic padding flags for each input dimension.
          ceil_out_dim (default False): ceiling while computing output size
          include_pad(default False): include pad while average pooling
          name (str, optional): the name of the Function instance in the network
      Returns:
          :class:`~cntk.ops.functions.Function`
      '''
      from cntk.cntk_py import pooling
      operand = sanitize_input(operand)
      pooling_window_shape, strides, auto_padding = sanitize_pooling_args(pooling_window_shape, strides, auto_padding)
      return pooling(operand, pooling_type, pooling_window_shape, strides, auto_padding,
                     ceil_out_dim, include_pad, name)
function_tokens: def pooling ( operand , pooling_type , pooling_window_shape , strides = ( 1 , ) , auto_padding = [ False ] , ceil_out_dim = False , include_pad = False , name = '' ) : from cntk . cntk_py import pooling operand = sanitize_input ( operand ) pooling_window_shape , strides , auto_padding = sanitize_pooling_args ( pooling_window_shape , strides , auto_padding ) return pooling ( operand , pooling_type , pooling_window_shape , strides , auto_padding , ceil_out_dim , include_pad , name )