code: def train_step(
      model: Any,
      optimizer: Any,
      x: Tensor,
      edge_index: Tensor,
      y: Tensor,
      bs: int,
  ) -> float:
      optimizer.zero_grad()
      out = model(x, edge_index)[:bs]
      loss = F.cross_entropy(out, y[:bs].squeeze())
      loss.backward()
      optimizer.step()
      return float(loss)
  
imports: import copy
  import os
  import os.path
  from typing import Any, Tuple, Union
  import intel_extension_for_pytorch
  import oneccl_bindings_for_pytorch
  import torch
  import torch.distributed
  import torch.nn.functional
  from ogb.nodeproppred import Evaluator, PygNodePropPredDataset
  from torch import Tensor
  from torch.nn import Linear
  from torch.nn.parallel import DistributedDataParallel
  from tqdm import tqdm
  from torch_geometric.loader import NeighborLoader
  from torch_geometric.nn import GATConv
  from torch_geometric.profile import get_stats_summary, profileit
label_desc: Use image node_id 1 for calling a global function with example usage: train_step(model, optimizer, x, edge_index, y, bs) and returns: float