Tensorflow-GNN model.fit() error while training a skeleton-based GNN (index error)

I am using TFGNN library to build a skeleton based graph neural network for action recognition and while running a simple model I keep getting the following error. The model is simple and it is adapted from the official colab

The input GraphSchema is the following:

GraphTensorSpec({'context': ContextSpec({'features': {}, 'sizes': TensorSpec(shape=(1,), dtype=tf.int32, name=None)}, TensorShape([]), tf.int32, None), 'node_sets': {'body': NodeSetSpec({'features': {'x_dim': TensorSpec(shape=(None, 1), dtype=tf.float32, name=None), 'z_dim': TensorSpec(shape=(None, 1), dtype=tf.float32, name=None), 'y_dim': TensorSpec(shape=(None, 1), dtype=tf.float32, name=None)}, 'sizes': TensorSpec(shape=(1,), dtype=tf.int32, name=None)}, TensorShape([]), tf.int32, None)}, 'edge_sets': {'bones': EdgeSetSpec({'features': {}, 'sizes': TensorSpec(shape=(1,), dtype=tf.int32, name=None), 'adjacency': AdjacencySpec({'#index.0': TensorSpec(shape=(None,), dtype=tf.int32, name=None), '#index.1': TensorSpec(shape=(None,), dtype=tf.int32, name=None)}, TensorShape([]), tf.int32, {'#index.0': 'body', '#index.1': 'body'})}, TensorShape([]), tf.int32, None)}}, TensorShape([]), tf.int32, None)

The model is the following:

def _build_model(
    # To be called with the build_model_graph_tensor_spec from above.
    graph_tensor_spec,
    # Dimensions of initial states.
    node_dim=128,
    # Dimensions for message passing.
    message_dim=128,
    next_state_dim=128,
    # Dimension for the logits.
    num_classes=3,
    # Other hyperparameters.
    l2_regularization=6e-6,
    dropout_rate=0.2,
    use_layer_normalization=True,
):
  # Model building with Keras's Functional API starts with an input object
  # (a placeholder for future inputs). This works for composite tensors, too.
  graph = input_graph = tf.keras.layers.Input(type_spec=graph_tensor_spec)
  graph = graph.merge_batch_to_components()

  def set_initial_node_state(node_set, node_set_name):
      if node_set_name == "body":
          feature_x_embedding = tf.keras.layers.Dense(node_dim, activation="relu")
          feature_y_embedding = tf.keras.layers.Dense(node_dim, activation="relu")
          feature_z_embedding = tf.keras.layers.Dense(node_dim, activation="relu")
          concatenated_features = tf.keras.layers.Concatenate()(
              [feature_x_embedding(node_set["x_dim"]),
              feature_y_embedding(node_set["y_dim"]),
              feature_z_embedding(node_set["z_dim"])])
          return concatenated_features
     
  graph = tfgnn.keras.layers.MapFeatures(
      node_sets_fn=set_initial_node_state, name="init_states")(graph)

  # Abbreviations for repeated building blocks in the GNN.
  def dense(units, *, use_layer_normalization=False):
    """A Dense layer with regularization (L2 and Dropout) and normalization."""
    regularizer = tf.keras.regularizers.l2(l2_regularization)
    result = tf.keras.Sequential([
        tf.keras.layers.Dense(
            units,
            activation="relu",
            use_bias=True,
            kernel_regularizer=regularizer,
            bias_regularizer=regularizer),
        tf.keras.layers.Dropout(dropout_rate)])
    if use_layer_normalization:
      result.add(tf.keras.layers.LayerNormalization())
    return result

  for i in range(4):
    graph = tfgnn.keras.layers.GraphUpdate(
    node_sets={
        "body": tfgnn.keras.layers.NodeSetUpdate(
            {"bones": tfgnn.keras.layers.SimpleConv(
                  tf.keras.layers.Dense(128, "relu"), 
                  "mean",
                  receiver_tag=tfgnn.TARGET)},
            tfgnn.keras.layers.NextStateFromConcat(tf.keras.layers.Dense(128)))
        }
    )(graph)

  root_states = tfgnn.keras.layers.ReadoutFirstNode(node_set_name="body")(graph)
  logits = tf.keras.layers.Dense(num_classes)(root_states)

  return tf.keras.Model(input_graph, logits)

It returns the following error when running on the dataset

Node: 'while/model_1/graph_update_4/node_set_update_4/simple_conv_4/UnsortedSegmentMean/UnsortedSegmentSum'
segment_ids[44] = 25 is out of range [0, 25)
     [[{{node while/model_1/graph_update_4/node_set_update_4/simple_conv_4/UnsortedSegmentMean/UnsortedSegmentSum}}]] [Op:__inference_train_function_10449]

The dimension of the node_set body is 25, while the dimension of the edge_set bones is 24.

I have tried re-modelling the graph structure and changing the layers of the graph update.

The failure is not model specific: it is data-specific.

In particular, your edge set "bones" has indices (e.g., in graph.edge_sets['bones'].adjacency.source or in graph.edge_sets['bones'].adjacency.target) that are larger than the number of available ("body") nodes given in the same GraphTensor.