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 _set_parameters(self):
      for key in self.W_i:
          glorot(self.W_i[key])
      for key in self.W_f:
          glorot(self.W_f[key])
      for key in self.W_c:
          glorot(self.W_c[key])
      for key in self.W_o:
          glorot(self.W_o[key])
      for key in self.b_i:
          glorot(self.b_i[key])
      for key in self.b_f:
          glorot(self.b_f[key])
      for key in self.b_c:
          glorot(self.b_c[key])
      for key in self.b_o:
          glorot(self.b_o[key])
  
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 7 for calling the HeteroGCLSTM obj's underlying member method code with example usage: obj._set_parameters() without return types