code: def check_dynamo(backend, device, err_msg):
      import torch
  
      if device == "cuda" and not torch.cuda.is_available():
          print(
              f"CUDA not available -- skipping CUDA check on {backend} backend\n"
          )
          return
  
      try:
          import torch._dynamo as dynamo
  
          if device == "cuda":
              from torch.utils._triton import has_triton
  
              if not has_triton():
                  print(
                      f"WARNING: CUDA available but triton cannot be used. "
                      f"Your GPU may not be supported. "
                      f"Skipping CUDA check on {backend} backend\n"
                  )
                  return
  
          dynamo.reset()
  
          @dynamo.optimize(backend, nopython=True)
          def fn(x):
              return x + x
  
          class Module(torch.nn.Module):
              def forward(self, x):
                  return x + x
  
          mod = Module()
          opt_mod = dynamo.optimize(backend, nopython=True)(mod)
  
          for f in (fn, opt_mod):
              x = torch.randn(10, 10).to(device)
              x.requires_grad = True
              y = f(x)
              torch.testing.assert_close(y, x + x)
              z = y.sum()
              z.backward()
              torch.testing.assert_close(x.grad, 2 * torch.ones_like(x))
      except Exception:
          sys.stderr.write(
              traceback.format_exc() + "\n" + err_msg + "\n\n"
          )
          sys.exit(1)
  
file_path: pytorch/tools/dynamo/verify_dynamo.py
imports: import os
  import re
  import subprocess
  import sys
  import traceback
  import warnings
label_desc: Use image node_id 7 for calling a global function with example usage: check_dynamo(backend, device, err_msg) and returns: unknown, unknown