class_docstr: A batch iterator object to contain a dynamic heterogeneous graph with a
  changing edge set and weights . The node labels
  (target) are also dynamic. The iterator returns a single discrete temporal
  snapshot for a time period (e.g. day or week). This single snapshot is a
  Pytorch Geometric Batch object. Between two temporal snapshots the edges,
  batch memberships, edge weights, target matrices and optionally passed
  attributes might change.
  
  Args:
      edge_index_dicts (Sequence of dictionaries where keys=Tuples and values=Numpy arrays):
       Sequence of relation type tuples and their edge index tensors.
      edge_weight_dicts (Sequence of dictionaries where keys=Tuples and values=Numpy arrays):
       Sequence of relation type tuples and their edge weight tensors.
      feature_dict (Dictionary of keys=Strings and values=Numpy arrays): Node type tuples
       and their node feature tensor.
      target_dicts (Sequence of dictionaries where keys=Strings and values=Numpy arrays):
       Sequence of node types and their label (target) tensors.
      batch_dicts (Sequence of dictionaries where keys=Strings and values=Numpy arrays):
       Sequence of batch index tensor for each node type.
      **kwargs (optional Sequence of dictionaries where keys=Strings and values=Numpy arrays): Sequence
       of node types and their additional attributes.
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code: def _get_target(self, time_index: int):
      if self.target_dicts[time_index] is None:
          return self.target_dicts[time_index]
      else:
          return {
              key: torch.FloatTensor(value)
              if value.dtype.kind == "f"
              else torch.LongTensor(value)
              if value.dtype.kind == "i"
              else value
              for key, value in self.target_dicts[time_index].items()
              if value is not None
          }
  
file_path: pytorch_geometric_temporal/torch_geometric_temporal/signal/dynamic_hetero_graph_static_signal_batch.py
imports: import torch
  import numpy
  from typing import Sequence, Dict, Union, Tuple
  from torch_geometric.data import HeteroData, Batch
label_desc: Use image node_id 8 for calling the DynamicHeteroGraphStaticSignalBatch obj's underlying member method code with example usage: obj._get_target(time_index) and returns: self, unknown