code: def generate_data():
      global cpu
      if cpu:
          return (
              torch.randn(args.batch_size, args.input_width).to(
                  torch.float32
              ),
              torch.randint(args.output_width, (args.batch_size,)),
          )
      else:
          return (
              torch.randn(args.batch_size, args.input_width)
              .to(torch.float32)
              .cuda(),
              torch.randint(
                  args.output_width, (args.batch_size,)
              ).cuda(),
          )
  
file_path: bert-ultrafast/benchmark_pytorch/main.py
imports: import torch
  import warnings
  import sqlite3
  from moe import MoENetwork
  from fff import FF
  from fff import FFF
  from fff import FFF_Sparse
  import time
  import argparse
  import torch._dynamo
  import numpy
label_desc: Use image node_id 1 for calling a global function with example usage: generate_data() and returns: torch,  torch, torch,  torch