parameters: (self, transformation_cache, state_manager)
return_statement: return sparse_ops.sparse_cross_hashed(
          inputs=feature_tensors,
          num_buckets=self.hash_bucket_size,
          hash_key=self.hash_key)
docstring: Generates a hashed sparse cross from the input tensors.
docstring_summary: Generates a hashed sparse cross from the input tensors.
docstring_tokens: Generates a hashed sparse cross from the input tensors .
function: def transform_feature(self, transformation_cache, state_manager):
      """Generates a hashed sparse cross from the input tensors."""
      feature_tensors = []
      for key in _collect_leaf_level_keys(self):
        if isinstance(key, six.string_types):
          feature_tensors.append(transformation_cache.get(key, state_manager))
        elif isinstance(key, (fc_old._CategoricalColumn, CategoricalColumn)):  # pylint: disable=protected-access
          ids_and_weights = key.get_sparse_tensors(transformation_cache,
                                                   state_manager)
          if ids_and_weights.weight_tensor is not None:
            raise ValueError(
                'crossed_column does not support weight_tensor, but the given '
                'column populates weight_tensor. '
                'Given column: {}'.format(key.name))
          feature_tensors.append(ids_and_weights.id_tensor)
        else:
          raise ValueError('Unsupported column type. Given: {}'.format(key))
      return sparse_ops.sparse_cross_hashed(
          inputs=feature_tensors,
          num_buckets=self.hash_bucket_size,
          hash_key=self.hash_key)
function_tokens: def transform_feature ( self , transformation_cache , state_manager ) : feature_tensors = [ ] for key in _collect_leaf_level_keys ( self ) : if isinstance ( key , six . string_types ) : feature_tensors . append ( transformation_cache . get ( key , state_manager ) ) elif isinstance ( key , ( fc_old . _CategoricalColumn , CategoricalColumn ) ) : # pylint: disable=protected-access ids_and_weights = key . get_sparse_tensors ( transformation_cache , state_manager ) if ids_and_weights . weight_tensor is not None : raise ValueError ( 'crossed_column does not support weight_tensor, but the given ' 'column populates weight_tensor. ' 'Given column: {}' . format ( key . name ) ) feature_tensors . append ( ids_and_weights . id_tensor ) else : raise ValueError ( 'Unsupported column type. Given: {}' . format ( key ) ) return sparse_ops . sparse_cross_hashed ( inputs = feature_tensors , num_buckets = self . hash_bucket_size , hash_key = self . hash_key )