class_docstr: An implementation of the Diffusion Convolutional Gated Recurrent Unit.
  For details see: `"Diffusion Convolutional Recurrent Neural Network:
  Data-Driven Traffic Forecasting" <https://arxiv.org/abs/1707.01926>`_
  
  Args:
      in_channels (int): Number of input features.
      out_channels (int): Number of output features.
      K (int): Filter size :math:`K`.
      bias (bool, optional): If set to :obj:`False`, the layer
          will not learn an additive bias (default :obj:`True`)
code: def _calculate_reset_gate(self, X, edge_index, edge_weight, H):
      R = torch.cat([X, H], dim=1)
      R = self.conv_x_r(R, edge_index, edge_weight)
      R = torch.sigmoid(R)
      return R
  
imports: import math
  import torch
  from torch_geometric.utils import to_dense_adj, dense_to_sparse
  from torch_geometric.nn.conv import MessagePassing
label_desc: Use image node_id 8 for calling the DCRNN obj's underlying member method code with example usage: obj._calculate_reset_gate(X, edge_index, edge_weight, H) and returns: R