code: def from_batch(batch: GraphData) -> List[GraphData]:
      """
      Convert a batch consisting of several GraphData instances to a list of GraphData instances.
  
      Parameters
      ----------
      batch : GraphData
          The source batch to be split.
  
      Returns
      -------
      list
          A list containing all the GraphData instances contained in the source batch.
      """
  
      num_nodes = batch._batch_num_nodes
      num_edges = batch._batch_num_edges
      all_edges = batch.get_all_edges()
      batch_size = batch.batch_size
      ret = []
      cum_n_nodes = 0
      cum_n_edges = 0
  
      # Construct graph respectively
      for i in range(batch_size):
          g = GraphData(device=batch.device)
          g.add_nodes(num_nodes[i])
          edges = all_edges[cum_n_edges : cum_n_edges + num_edges[i]]
          src, tgt = [e[0] - cum_n_nodes for e in edges], [
              e[1] - cum_n_nodes for e in edges
          ]
          g.add_edges(src, tgt)
          cum_n_edges += num_edges[i]
          cum_n_nodes += num_nodes[i]
          ret.append(g)
  
      # Add node and edge features
  
      for k, v in batch._node_features.items():
          if v is not None:
              cum_n_nodes = 0  # Cumulative node numbers
              for i in range(batch_size):
                  ret[i].node_features[k] = v[
                      cum_n_nodes : cum_n_nodes + num_nodes[i]
                  ]
                  cum_n_nodes += num_nodes[i]
  
      for k, v in batch._edge_features.items():
          if v is not None:
              cum_n_edges = 0  # Cumulative edge numbers
              for i in range(batch_size):
                  ret[i].edge_features[k] = v[
                      cum_n_edges : cum_n_edges + num_edges[i]
                  ]
                  cum_n_edges += num_edges[i]
  
      cum_n_nodes = 0
      cum_n_edges = 0
  
      # Add node and edge attributes
      for graph_cnt in range(batch_size):
          for num_graph_nodes in range(num_nodes[graph_cnt]):
              ret[graph_cnt].node_attributes[
                  num_graph_nodes
              ] = batch.node_attributes[cum_n_nodes + num_graph_nodes]
          for num_graph_edges in range(num_edges[graph_cnt]):
              ret[graph_cnt].edge_attributes[
                  num_graph_edges
              ] = batch.edge_attributes[cum_n_edges + num_graph_edges]
          cum_n_edges += num_edges[graph_cnt]
          cum_n_nodes += num_nodes[graph_cnt]
  
      return ret
  
imports: import os
  import warnings
  from collections import namedtuple
  from typing import Any, Callable, Dict, List, Tuple, Union
  import dgl
  import scipy.sparse
  import torch
  from torch.nn.utils.rnn import pad_sequence
  from .utils import EdgeNotFoundException, SizeMismatchException, check_and_expand, entail_zero_padding, int_to_list, slice_to_list
  from .views import BatchEdgeFeatView, BatchNodeFeatView, EdgeView, NodeFeatView, NodeView
label_desc: Use image node_id 3 for calling a global function with example usage: from_batch(batch) and returns: ret