code: def extend_minibatch_with(
      self,
      tensorized_datapoint: TensorizedGraphData[
          TTensorizedNodeData, TTensorizedEdgeData
      ],
      partial_minibatch: Dict[str, Any],
  ) -> bool:
      continue_extending = True
      for (
          node_tensorized_info
      ) in tensorized_datapoint.node_tensorized_data:
          continue_extending &= (
              self.__node_embedding_model.extend_minibatch_with(
                  node_tensorized_info,
                  partial_minibatch["node_data_mb"],
              )
          )
  
      graph_idx = len(partial_minibatch["num_nodes_per_graph"])
  
      adj_list = partial_minibatch["adjacency_lists"]
      tensorized_edge_feature_data = partial_minibatch[
          "edge_feature_data"
      ]
      nodes_in_mb_so_far = partial_minibatch["num_nodes_in_mb"]
  
      datapoint_edge_features = tensorized_datapoint.edge_features
      if datapoint_edge_features is None:
          datapoint_edge_features = [None for _ in range(len(adj_list))]
  
      for (
          sample_adj_list_for_edge_type,
          edge_features,
          mb_adj_lists_for_edge_type,
          mb_edge_feature_data,
      ) in zip(
          tensorized_datapoint.adjacency_lists,
          datapoint_edge_features,
          adj_list,
          tensorized_edge_feature_data,
      ):
          mb_adj_lists_for_edge_type[0].append(
              sample_adj_list_for_edge_type[0] + nodes_in_mb_so_far
          )
          mb_adj_lists_for_edge_type[1].append(
              sample_adj_list_for_edge_type[1] + nodes_in_mb_so_far
          )
          if self.__edge_embedding_model is not None:
              for edge_feature in edge_features:
                  self.__edge_embedding_model.extend_minibatch_with(
                      edge_feature, mb_edge_feature_data
                  )
  
      for (
          ref_name,
          ref_nodes,
      ) in tensorized_datapoint.reference_nodes.items():
          partial_minibatch["reference_node_graph_idx"][
              ref_name
          ].extend(graph_idx for _ in range(len(ref_nodes)))
          partial_minibatch["reference_node_ids"][ref_name].append(
              ref_nodes + nodes_in_mb_so_far
          )
  
      partial_minibatch["num_nodes_per_graph"].append(
          tensorized_datapoint.num_nodes
      )
      partial_minibatch["num_nodes_in_mb"] = (
          nodes_in_mb_so_far + tensorized_datapoint.num_nodes
      )
      return (
          partial_minibatch["num_nodes_in_mb"]
          < self.stop_extending_minibatch_after_num_nodes
      )
  
imports: from typing_extensions import Final
  import logging
  import numpy
  import torch
  from collections import defaultdict
  from torch import nn
  from typing import Any, Callable, Dict, Iterable, Iterator, List, Optional, Set, Tuple, TypeVar, Union
  from ptgnn.baseneuralmodel import AbstractNeuralModel, ModuleWithMetrics
  from ptgnn.baseneuralmodel.utils.data import enforce_not_None
  from ptgnn.neuralmodels.gnn.messagepassing.abstractmessagepassing import AbstractMessagePassingLayer
  from ptgnn.neuralmodels.gnn.structs import GnnOutput, GraphData, TensorizedGraphData
label_desc: Use image node_id 11 for calling the GraphNeuralNetworkModel obj's underlying member method code with example usage: obj.extend_minibatch_with(tensorized_datapoint, partial_minibatch) and returns: unknown