parameters: (self, transformation_cache, state_manager)
return_statement: return self._transform_id_weight_pair(id_weight_pair)
docstring: Returns dense `Tensor` representing feature.
  
      Args:
        transformation_cache: A `FeatureTransformationCache` object to access
          features.
        state_manager: A `StateManager` to create / access resources such as
          lookup tables.
  
      Returns:
        Transformed feature `Tensor`.
  
      Raises:
        ValueError: if input rank is not known at graph building time.
docstring_summary: Returns dense `Tensor` representing feature.
docstring_tokens: Returns dense Tensor representing feature .
function: def transform_feature(self, transformation_cache, state_manager):
      """Returns dense `Tensor` representing feature.
  
      Args:
        transformation_cache: A `FeatureTransformationCache` object to access
          features.
        state_manager: A `StateManager` to create / access resources such as
          lookup tables.
  
      Returns:
        Transformed feature `Tensor`.
  
      Raises:
        ValueError: if input rank is not known at graph building time.
      """
      id_weight_pair = self.categorical_column.get_sparse_tensors(
          transformation_cache, state_manager)
      return self._transform_id_weight_pair(id_weight_pair)
function_tokens: def transform_feature ( self , transformation_cache , state_manager ) : id_weight_pair = self . categorical_column . get_sparse_tensors ( transformation_cache , state_manager ) return self . _transform_id_weight_pair ( id_weight_pair )