parameters: (self, y, axis=None)
return_statement: return - self.padding[axis] + y * self.stride[axis] + \
        (self.size[axis] - 1) / 2
docstring: Computes the center of the receptive field that generated a feature.
  
      Args:
        y: An array of feature coordinates with shape `(..., d)`, where `d` is the
          number of dimensions of the coordinates.
        axis: The dimensions for which to compute the input center coordinates.
          If `None` (the default), compute the input center coordinates for all
          dimensions.
  
      Returns:
        x: Center of the receptive field that generated the features, at the input
          of the network.
  
      Raises:
        ValueError: If the number of dimensions of the feature coordinates does
          not match the number of elements in `axis`.
docstring_summary: Computes the center of the receptive field that generated a feature.
docstring_tokens: Computes the center of the receptive field that generated a feature .
function: def compute_input_center_coordinates(self, y, axis=None):
      """
      Computes the center of the receptive field that generated a feature.
  
      Args:
        y: An array of feature coordinates with shape `(..., d)`, where `d` is the
          number of dimensions of the coordinates.
        axis: The dimensions for which to compute the input center coordinates.
          If `None` (the default), compute the input center coordinates for all
          dimensions.
  
      Returns:
        x: Center of the receptive field that generated the features, at the input
          of the network.
  
      Raises:
        ValueError: If the number of dimensions of the feature coordinates does
          not match the number of elements in `axis`.
      """
      # Use all dimensions.
      if axis is None:
        axis = range(self.size.size)
      # Ensure axis is a list because tuples have different indexing behavior.
      axis = list(axis)
      y = np.asarray(y)
      if y.shape[-1] != len(axis):
        raise ValueError("Dimensionality of the feature coordinates `y` (%d) "
                         "does not match dimensionality of `axis` (%d)" %
                         (y.shape[-1], len(axis)))
      return - self.padding[axis] + y * self.stride[axis] + \
        (self.size[axis] - 1) / 2
function_tokens: def compute_input_center_coordinates ( self , y , axis = None ) : # Use all dimensions. if axis is None : axis = range ( self . size . size ) # Ensure axis is a list because tuples have different indexing behavior. axis = list ( axis ) y = np . asarray ( y ) if y . shape [ - 1 ] != len ( axis ) : raise ValueError ( "Dimensionality of the feature coordinates `y` (%d) " "does not match dimensionality of `axis` (%d)" % ( y . shape [ - 1 ] , len ( axis ) ) ) return - self . padding [ axis ] + y * self . stride [ axis ] + ( self . size [ axis ] - 1 ) / 2