parameters: (self, transformation_cache, state_manager)
return_statement: return self._get_sparse_tensors_helper(sparse_tensors)
docstring: Returns an IdWeightPair.
  
      `IdWeightPair` is a pair of `SparseTensor`s which represents ids and
      weights.
  
      `IdWeightPair.id_tensor` is typically a `batch_size` x `num_buckets`
      `SparseTensor` of `int64`. `IdWeightPair.weight_tensor` is either a
      `SparseTensor` of `float` or `None` to indicate all weights should be
      taken to be 1. If specified, `weight_tensor` must have exactly the same
      shape and indices as `sp_ids`. Expected `SparseTensor` is same as parsing
      output of a `VarLenFeature` which is a ragged matrix.
  
      Args:
        transformation_cache: A `FeatureTransformationCache` object to access
          features.
        state_manager: A `StateManager` to create / access resources such as
          lookup tables.
docstring_summary: Returns an IdWeightPair.
docstring_tokens: Returns an IdWeightPair .
function: def get_sparse_tensors(self, transformation_cache, state_manager):
      """Returns an IdWeightPair.
  
      `IdWeightPair` is a pair of `SparseTensor`s which represents ids and
      weights.
  
      `IdWeightPair.id_tensor` is typically a `batch_size` x `num_buckets`
      `SparseTensor` of `int64`. `IdWeightPair.weight_tensor` is either a
      `SparseTensor` of `float` or `None` to indicate all weights should be
      taken to be 1. If specified, `weight_tensor` must have exactly the same
      shape and indices as `sp_ids`. Expected `SparseTensor` is same as parsing
      output of a `VarLenFeature` which is a ragged matrix.
  
      Args:
        transformation_cache: A `FeatureTransformationCache` object to access
          features.
        state_manager: A `StateManager` to create / access resources such as
          lookup tables.
      """
      sparse_tensors = self.categorical_column.get_sparse_tensors(
          transformation_cache, state_manager)
      return self._get_sparse_tensors_helper(sparse_tensors)
function_tokens: def get_sparse_tensors ( self , transformation_cache , state_manager ) : sparse_tensors = self . categorical_column . get_sparse_tensors ( transformation_cache , state_manager ) return self . _get_sparse_tensors_helper ( sparse_tensors )