parameters: (inputs, factor, scope=None)
docstring: Subsamples the input along the spatial dimensions.
  
      Args:
        inputs: A `Tensor` of size [batch, height_in, width_in, channels].
        factor: The subsampling factor.
        scope: Optional variable_scope.
  
      Returns:
        output: A `Tensor` of size [batch, height_out, width_out, channels] with the
          input, either intact (if factor == 1) or subsampled (if factor > 1).
docstring_summary: Subsamples the input along the spatial dimensions.
docstring_tokens: Subsamples the input along the spatial dimensions .
function: def subsample(inputs, factor, scope=None):
      """Subsamples the input along the spatial dimensions.
  
      Args:
        inputs: A `Tensor` of size [batch, height_in, width_in, channels].
        factor: The subsampling factor.
        scope: Optional variable_scope.
  
      Returns:
        output: A `Tensor` of size [batch, height_out, width_out, channels] with the
          input, either intact (if factor == 1) or subsampled (if factor > 1).
      """
      if factor == 1:
          return inputs
      else:
          return slim.max_pool2d(inputs, [1, 1], stride=factor, scope=scope)
function_tokens: def subsample ( inputs , factor , scope = None ) : if factor == 1 : return inputs else : return slim . max_pool2d ( inputs , [ 1 , 1 ] , stride = factor , scope = scope )