class_docstr: A plain old python object modeling a batch of
  :class:`deepsnap.graph.Graph` objects as one big (disconnected) graph,
  with :class:`torch_geometric.data.Data` being the
  base class that all its methods can also be used here.
  In addition, graphs can be reconstructed via the assignment vector
  :obj:`batch`, which maps each node to its respective graph identifier.
  
  .. note::
  
      For more detailed use of :class:`deepsnap.batch.Batch`, see the `examples 
      <https://github.com/snap-stanford/deepsnap/tree/master/examples>`_ folder.
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docstr: Compared to :meth:`apply_transform`, this allows multiple graph objects
  to be returned by the given transform function.
  
  Args:
      transform (callable): (Multiple return value) tranformation function
          applied to each graph object. It needs to return a tuple of
          Graph objects.
      update_tensors (bool): Whether use nx graph to update tensor attributes.
      update_graphs (bool): Whether use tensor attributes to update nx graphs.
      deep_copy (bool): :obj:`True` if a new deep copy of batch is returned.
          This option allows modifying the batch of graphs without
          changing the graphs in the original dataset.
      kwargs: Parameters used in the transform function for each
          :class:`deepsnap.graph.Graph`.
  
  Returns:
      A tuple of batch objects. The i-th batch object contains the i-th
      return value of the transform function applied to all graphs
      in the batch.
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code: def apply_transform_multi(
      self,
      transform,
      update_tensors: bool = True,
      update_graphs: bool = False,
      deep_copy: bool = False,
      **kwargs,
  ):
      r"""
      Compared to :meth:`apply_transform`, this allows multiple graph objects
      to be returned by the given transform function.
  
      Args:
          transform (callable): (Multiple return value) tranformation function
              applied to each graph object. It needs to return a tuple of
              Graph objects.
          update_tensors (bool): Whether use nx graph to update tensor attributes.
          update_graphs (bool): Whether use tensor attributes to update nx graphs.
          deep_copy (bool): :obj:`True` if a new deep copy of batch is returned.
              This option allows modifying the batch of graphs without
              changing the graphs in the original dataset.
          kwargs: Parameters used in the transform function for each
              :class:`deepsnap.graph.Graph`.
  
      Returns:
          A tuple of batch objects. The i-th batch object contains the i-th
          return value of the transform function applied to all graphs
          in the batch.
      """
      g_lists = zip(
          *[
              Graph(graph).apply_transform_multi(
                  transform,
                  update_tensors,
                  update_graphs,
                  deep_copy,
                  **kwargs,
              )
              for graph in self.G
          ]
      )
      return (self.from_data_list(g_list) for g_list in g_lists)
  
file_path: deepsnap/deepsnap/batch.py
imports: import torch
  from deepsnap.graph import Graph
  from deepsnap.hetero_graph import HeteroGraph
  from typing import Callable, Dict, List
label_desc: Use image node_id 11 for calling the Batch obj's underlying member method code with example usage: obj.apply_transform_multi(transform, update_tensors, update_graphs, deep_copy) and returns: unknown