docstr: x = Node features for T time steps
  edge_index = Graph edge indices
docstr_tok: ["x","=","Node","features","for","T","time","steps","edge_index","=","Graph","edge","indices"]
code: def forward(self, x, edge_index):
      """
      x = Node features for T time steps
      edge_index = Graph edge indices
      """
      h = self.tgnn(
          x, edge_index
      )  # x [b, 207, 2, 12]  returns h [b, 207, 12]
      h = F.relu(h)
      h = self.linear(h)
      return h
  
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 2 for calling the TemporalGNN obj's underlying member method code with example usage: obj.forward(x, edge_index) and returns: h