parameters: (self, transformation_cache, state_manager)
return_statement: return input_tensor
docstring: See `FeatureColumn` base class.
  
      In this case, we apply the `normalizer_fn` to the input tensor.
  
      Args:
        transformation_cache: A `FeatureTransformationCache` object to access
          features.
        state_manager: A `StateManager` to create / access resources such as
          lookup tables.
  
      Returns:
        Normalized input tensor.
docstring_summary: See `FeatureColumn` base class.
docstring_tokens: See FeatureColumn base class .
function: def transform_feature(self, transformation_cache, state_manager):
      """See `FeatureColumn` base class.
  
      In this case, we apply the `normalizer_fn` to the input tensor.
  
      Args:
        transformation_cache: A `FeatureTransformationCache` object to access
          features.
        state_manager: A `StateManager` to create / access resources such as
          lookup tables.
  
      Returns:
        Normalized input tensor.
      """
      input_tensor = transformation_cache.get(self.key, state_manager)
      if self.normalizer_fn is not None:
        input_tensor = self.normalizer_fn(input_tensor)
      return input_tensor
function_tokens: def transform_feature ( self , transformation_cache , state_manager ) : input_tensor = transformation_cache . get ( self . key , state_manager ) if self . normalizer_fn is not None : input_tensor = self . normalizer_fn ( input_tensor ) return input_tensor