code: def full_batch_inference(model, data):
      model.eval()
      if hasattr(data, "adj_t"):
          edge_index = data.adj_t
      else:
          edge_index = data.edge_index
      return model(data.x, edge_index)
  
file_path: pytorch_geometric/benchmark/inference/inference_benchmark.py
imports: import argparse
  import warnings
  from collections import defaultdict
  from contextlib import nullcontext
  import torch
  from benchmark.utils import emit_itt, get_dataset_with_transformation, get_model, get_split_masks, save_benchmark_data, test, write_to_csv
  from torch_geometric.loader import NeighborLoader
  from torch_geometric.nn import PNAConv
  from torch_geometric.profile import rename_profile_file, timeit, torch_profile, xpu_profile
label_desc: Use image node_id 1 for calling a global function with example usage: full_batch_inference(model, data) and returns: model