code: def __init__(self, node_features, periods, batch_size):
      super(TemporalGNN, self).__init__()
      # Attention Temporal Graph Convolutional Cell
      self.tgnn = A3TGCN2(
          in_channels=node_features,
          out_channels=32,
          periods=periods,
          batch_size=batch_size,
      )  # node_features=2, periods=12
      # Equals single-shot prediction
      self.linear = torch.nn.Linear(32, periods)
  
file_path: pytorch_geometric_temporal/examples/recurrent/a3tgcn2_example.py
imports: import numpy
  import matplotlib.pyplot
  import seaborn
  import torch
  import torch.nn.functional
  from torch_geometric.nn import GCNConv
  from torch_geometric_temporal.nn.recurrent import A3TGCN2
  from torch_geometric_temporal.dataset import METRLADatasetLoader
  from torch_geometric_temporal.signal import temporal_signal_split
label_desc: Use image node_id 1 to create a new TemporalGNN object from inherited base classes: torch.nn with example: obj = TemporalGNN(node_features, periods, batch_size)