class_docstr: An implementation THAT SUPPORTS BATCHES 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.
      batch_size (int): Size of the batch.
      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.
class_docstr_tok: ["An","implementation","THAT","SUPPORTS","BATCHES","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",".","batch_size","(","int",")",":","Size","of","the","batch",".","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","."]
code: def _calculate_update_gate(self, X, edge_index, edge_weight, H):
      Z = torch.cat(
          [self.conv_z(X, edge_index, edge_weight), H], axis=2
      )  # (b, 207, 64)
      Z = self.linear_z(Z)  # (b, 207, 32)
      Z = torch.sigmoid(Z)
  
      return Z
  
imports: import torch
  from torch_geometric.nn import GCNConv
label_desc: Use image node_id 7 for calling the TGCN2 obj's underlying member method code with example usage: obj._calculate_update_gate(X, edge_index, edge_weight, H) and returns: Z