class_docstr: A data iterator object to contain a dynamic heterogeneous graph with a
  changing edge set and weights. The feature set and 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 HeteroData Batch object. Between two temporal snapshots the edges,
  edge weights, the feature matrix, 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_dicts (Sequence of dictionaries where keys=Strings and values=Numpy arrays):
       Sequence of node types and their feature tensors.
      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 __init__(
      self,
      edge_index_dicts: Edge_Indices,
      edge_weight_dicts: Edge_Weights,
      feature_dicts: Node_Features,
      target_dicts: Targets,
      batch_dicts: Batches,
      **kwargs: Additional_Features,
  ):
      self.edge_index_dicts = edge_index_dicts
      self.edge_weight_dicts = edge_weight_dicts
      self.feature_dicts = feature_dicts
      self.target_dicts = target_dicts
      self.batch_dicts = batch_dicts
      self.additional_feature_keys = []
      for key, value in kwargs.items():
          setattr(self, key, value)
          self.additional_feature_keys.append(key)
      self._check_temporal_consistency()
      self._set_snapshot_count()
  
file_path: pytorch_geometric_temporal/torch_geometric_temporal/signal/dynamic_hetero_graph_temporal_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 1 to create a new DynamicHeteroGraphTemporalSignalBatch object from inherited base classes: object with example: obj = DynamicHeteroGraphTemporalSignalBatch(edge_index_dicts, edge_weight_dicts, feature_dicts, target_dicts, batch_dicts)