parameters: (self, columns_to_tensors)
docstring: Apply transformation and inserts it into columns_to_tensors.
  
      Args:
        columns_to_tensors: A mapping from feature columns to tensors. 'string'
          key means a base feature (not-transformed). It can have _FeatureColumn
          as a key too. That means that _FeatureColumn is already transformed.
docstring_summary: Apply transformation and inserts it into columns_to_tensors.
docstring_tokens: Apply transformation and inserts it into columns_to_tensors .
function: def insert_transformed_feature(self, columns_to_tensors):
      """Apply transformation and inserts it into columns_to_tensors.
  
      Args:
        columns_to_tensors: A mapping from feature columns to tensors. 'string'
          key means a base feature (not-transformed). It can have _FeatureColumn
          as a key too. That means that _FeatureColumn is already transformed.
      """
      # Transform the input tensor according to the normalizer function.
      input_tensor = self._normalized_input_tensor(columns_to_tensors[self.name])
      columns_to_tensors[self] = math_ops.to_float(input_tensor)
function_tokens: def insert_transformed_feature ( self , columns_to_tensors ) : # Transform the input tensor according to the normalizer function. input_tensor = self . _normalized_input_tensor ( columns_to_tensors [ self . name ] ) columns_to_tensors [ self ] = math_ops . to_float ( input_tensor )