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 _create_input_gate_parameters_and_layers(self):
      self.conv_i = HeteroConv(
          {
              edge_type: SAGEConv(
                  in_channels=(-1, -1),
                  out_channels=self.out_channels,
                  bias=self.bias,
              )
              for edge_type in self.metadata[1]
          }
      )
  
      self.W_i = nn.ParameterDict(
          {
              node_type: Parameter(
                  torch.Tensor(in_channels, self.out_channels)
              )
              for node_type, in_channels in self.in_channels_dict.items()
          }
      )
      self.b_i = nn.ParameterDict(
          {
              node_type: Parameter(torch.Tensor(1, self.out_channels))
              for node_type in self.in_channels_dict
          }
      )
  
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 2 for calling the HeteroGCLSTM obj's underlying member method code with example usage: obj._create_input_gate_parameters_and_layers() without return types