class_docstr: An implementation of the Temporal Graph Convolutional Gated Recurrent Cell.
  For details see this paper: `"T-GCN: A Temporal Graph ConvolutionalNetwork for
  Traffic Prediction." <https://arxiv.org/abs/1811.05320>`_
  
  Args:
      in_channels (int): Number of input features.
      out_channels (int): Number of output features.
      improved (bool): Stronger self loops. Default is False.
      cached (bool): Caching the message weights. Default is False.
      add_self_loops (bool): Adding self-loops for smoothing. Default is True.
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docstr: Making a forward pass. If edge weights are not present the forward pass
  defaults to an unweighted graph. If the hidden state matrix is not present
  when the forward pass is called it is initialized with zeros.
  
  Arg types:
      * **X** *(PyTorch Float Tensor)* - Node features.
      * **edge_index** *(PyTorch Long Tensor)* - Graph edge indices.
      * **edge_weight** *(PyTorch Long Tensor, optional)* - Edge weight vector.
      * **H** *(PyTorch Float Tensor, optional)* - Hidden state matrix for all nodes.
  
  Return types:
      * **H** *(PyTorch Float Tensor)* - Hidden state matrix for all nodes.
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code: def forward(
      self,
      X: torch.FloatTensor,
      edge_index: torch.LongTensor,
      edge_weight: torch.FloatTensor = None,
      H: torch.FloatTensor = None,
  ) -> torch.FloatTensor:
      """
      Making a forward pass. If edge weights are not present the forward pass
      defaults to an unweighted graph. If the hidden state matrix is not present
      when the forward pass is called it is initialized with zeros.
  
      Arg types:
          * **X** *(PyTorch Float Tensor)* - Node features.
          * **edge_index** *(PyTorch Long Tensor)* - Graph edge indices.
          * **edge_weight** *(PyTorch Long Tensor, optional)* - Edge weight vector.
          * **H** *(PyTorch Float Tensor, optional)* - Hidden state matrix for all nodes.
  
      Return types:
          * **H** *(PyTorch Float Tensor)* - Hidden state matrix for all nodes.
      """
      H = self._set_hidden_state(X, H)
      Z = self._calculate_update_gate(X, edge_index, edge_weight, H)
      R = self._calculate_reset_gate(X, edge_index, edge_weight, H)
      H_tilde = self._calculate_candidate_state(
          X, edge_index, edge_weight, H, R
      )
      H = self._calculate_hidden_state(Z, H, H_tilde)
      return H
  
imports: import torch
  from torch_geometric.nn import GCNConv
label_desc: Use image node_id 11 for calling the TGCN obj's underlying member method code with example usage: obj.forward(X, edge_index, edge_weight, H) and returns: H