tensorflow_gnn/graph/graph_tensor_ops.py [211:243]:
- - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - -
                          reduce_type: str = 'sum',
                          *,
                          feature_value: Optional[Field] = None,
                          feature_name: Optional[FieldName] = None) -> Field:
  """Aggregates (pools) node values to graph context.

  Given a particular node set (identified by `node_set_name`), this operation
  reduces node features to their corresponding graph component. For example,
  setting `reduce_type='sum'` computes the sum over the node features of each
  graph. (See the corresponding `broadcast_context_to_nodes()` mirror
  operation).

  The feature to fetch node values from is provided either by name (using
  `feature_name`) and found in the graph tensor itself, or provided explicitly
  (using `feature_value`). One of `feature_value` or `feature_name` must be
  specified.

  Args:
    graph_tensor: A scalar GraphTensor.
    node_set_name: A node set name.
    reduce_type: A pooling operation name, like 'sum', 'mean' or 'max'. For the
      list of supported values use `get_registered_reduce_operation_names()`.
      You may `register_reduce_operation(..)` to register new ops.
    feature_value: A ragged or dense node feature value.
    feature_name: A node feature name.

  Returns:
    Node value pooled to graph context. The first dimension size equals to the
    first context dimension (number of graph components), the higher dimensions
    do not change.
  """
  return _pool_to_context(
      graph_tensor,
- - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - -



tensorflow_gnn/graph/graph_tensor_ops.py [252:288]:
- - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - -
                          reduce_type: str = 'sum',
                          *,
                          feature_value: Optional[Field] = None,
                          feature_name: Optional[FieldName] = None) -> Field:
  """Aggregates (pools) edge values to graph context.

  Given a particular edge set (identified by `edge_set_name`), this operation
  reduces edge features to their corresponding graph component. For example,
  setting `reduce_type='sum'` computes the sum over the edge features of each
  graph. (See the corresponding `broadcast_context_to_edges()` mirror
  operation).

  The feature to fetch edge values from is provided either by name (using
  `feature_name`) and found in the graph tensor itself, or provided explicitly
  (using `feature_value`). One of `feature_value` or `feature_name` must be
  specified.

  (Note that in most cases the `feature_value` form will be used, because in a
  regular convolution, we will first broadcast over edges and combine the result
  of that with this function or a pooling over the nodes.)

  Args:
    graph_tensor: A scalar GraphTensor.
    edge_set_name: An edge set name.
    reduce_type: A pooling operation name, like 'sum', 'mean' or 'max'. For the
      list of supported values use `get_registered_reduce_operation_names()`.
      You may `register_reduce_operation(..)` to register new ops.
    feature_value: A ragged or dense edge feature value.
    feature_name: An edge feature name.

  Returns:
    A node value pooled to graph context. The first dimension size equals to the
    first context dimension (number of graph components), the higher dimensions
    do not change.
  """
  return _pool_to_context(
      graph_tensor,
- - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - -