code: def set_jit_fusion_options():
      """Set PyTorch JIT layer fusion options."""
      # flags required to enable jit fusion kernels
      TORCH_MAJOR = int(torch.__version__.split(".")[0])
      TORCH_MINOR = int(torch.__version__.split(".")[1])
      if (TORCH_MAJOR > 1) or (TORCH_MAJOR == 1 and TORCH_MINOR >= 10):
          # nvfuser
          torch._C._jit_set_profiling_executor(True)
          torch._C._jit_set_profiling_mode(True)
          torch._C._jit_override_can_fuse_on_cpu(False)
          torch._C._jit_override_can_fuse_on_gpu(False)
          torch._C._jit_set_texpr_fuser_enabled(False)
          torch._C._jit_set_nvfuser_enabled(True)
          torch._C._debug_set_autodiff_subgraph_inlining(False)
      else:
          # legacy pytorch fuser
          torch._C._jit_set_profiling_mode(False)
          torch._C._jit_set_profiling_executor(False)
          torch._C._jit_override_can_fuse_on_cpu(True)
          torch._C._jit_override_can_fuse_on_gpu(True)
  
      _warmup_jit_function()
  
imports: import random
  import os
  import time
  import numpy
  import torch
  from datetime import timedelta
  from megatron import fused_kernels
  from megatron import get_adlr_autoresume
  from megatron import get_args
  from megatron import get_tensorboard_writer
  from megatron.core import mpu, tensor_parallel
  from megatron.arguments import parse_args, validate_args
  from megatron.checkpointing import load_args_from_checkpoint
  from megatron.global_vars import set_global_variables
  from megatron.model.transformer import bias_dropout_add_fused_train
  from megatron.model.fused_bias_gelu import bias_gelu
label_desc: Use image node_id 8 for calling a global function with example usage: set_jit_fusion_options() without return types