class_docstr: An implementation similar to the Integrated Graph Convolutional Long Short Term
  Memory Cell for heterogeneous Graphs.
  
  Args:
      in_channels_dict (dict of keys=str and values=int): Dimension of each node's input features.
      out_channels (int): Number of output features.
      metadata (tuple): Metadata on node types and edge types in the graphs. Can be generated via PyG method
          :obj:`snapshot.metadata()` where snapshot is a single HeteroData object.
      bias (bool, optional): If set to :obj:`False`, the layer will not learn
          an additive bias. (default: :obj:`True`)
code: def __init__(
      self,
      in_channels_dict: dict,
      out_channels: int,
      metadata: tuple,
      bias: bool = True,
  ):
      super(HeteroGCLSTM, self).__init__()
  
      self.in_channels_dict = in_channels_dict
      self.out_channels = out_channels
      self.metadata = metadata
      self.bias = bias
      self._create_parameters_and_layers()
      self._set_parameters()
  
imports: import torch
  from torch.nn import Parameter
  from torch_geometric.nn import HeteroConv, SAGEConv
  from torch_geometric.nn.inits import glorot
  import torch.nn
label_desc: Use image node_id 1 to create a new HeteroGCLSTM object from inherited base classes: torch.nn with example: obj = HeteroGCLSTM(in_channels_dict, out_channels, metadata, bias)