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 layers.max_pool2d(inputs, [1, 1], stride=factor, scope=scope)
function_tokens: def subsample ( inputs , factor , scope = None ) : if factor == 1 : return inputs else : return layers . max_pool2d ( inputs , [ 1 , 1 ] , stride = factor , scope = scope )