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 __init__(
      self,
      in_channels: int,
      out_channels: int,
      K: int,
      bias: bool = True,
  ):
      super(DCRNN, self).__init__()
  
      self.in_channels = in_channels
      self.out_channels = out_channels
      self.K = K
      self.bias = bias
  
      self._create_parameters_and_layers()
  
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 1 to create a new DCRNN object from inherited base classes: torch.nn with example: obj = DCRNN(in_channels, out_channels, K, bias)