class_docstr: An implementation of the Adaptive Graph Convolutional Recurrent Unit.
  For details see: `"Adaptive Graph Convolutional Recurrent Network
  for Traffic Forecasting" <https://arxiv.org/abs/2007.02842>`_
  Args:
      number_of_nodes (int): Number of vertices.
      in_channels (int): Number of input features.
      out_channels (int): Number of output features.
      K (int): Filter size :math:`K`.
      embedding_dimensions (int): Number of node embedding dimensions.
code: def _set_hidden_state(self, X, H):
      if H is None:
          H = torch.zeros(X.shape[0], X.shape[1], self.out_channels).to(
              X.device
          )
      return H
  
file_path: pytorch_geometric_temporal/torch_geometric_temporal/nn/recurrent/agcrn.py
imports: import torch
  import torch.nn
  import torch.nn.functional
  from torch_geometric.nn.inits import glorot, zeros
label_desc: Use image node_id 3 for calling the AGCRN obj's underlying member method code with example usage: obj._set_hidden_state(X, H) and returns: H