code: def test_torch_as_tensor(
      *,
      dtype_x_dtype,
      on_device,
      fn_tree,
      frontend,
      test_flags,
      backend_fw,
  ):
      input_dtype, x, dtype = dtype_x_dtype
      # ToDo: fix get_castable_dtype to avoid the exceptions
      try:
          helpers.test_frontend_function(
              input_dtypes=input_dtype,
              backend_to_test=backend_fw,
              frontend=frontend,
              test_flags=test_flags,
              fn_tree=fn_tree,
              on_device=on_device,
              data=x,
              dtype=dtype,
              device=on_device,
          )
      except Exception as e:
          if any(
              error_string in str(e)
              for error_string in [
                  "overflow",
                  "too large to convert to",
              ]
          ):
              assume(False)
          else:
              raise
  
imports: from hypothesis import strategies, assume
  import math
  import numpy
  import ivy_tests.test_ivy.helpers
  import ivy_tests.test_ivy.helpers.globals
  from ivy_tests.test_ivy.helpers import handle_frontend_test, BackendHandler
label_desc: Use image node_id 9 for calling a global function with example usage: test_torch_as_tensor() without return types