code: def test_broadcast_arrays(
      *,
      arrays,
      input_dtypes,
      test_flags,
      backend_fw,
      fn_name,
      on_device,
  ):
      if backend_fw == "torch":
          for input_dtype in input_dtypes:
              if input_dtype == "bfloat16" or (
                  "uint" in input_dtype and "uint8" not in input_dtype
              ):
                  # Torch has no inference strategy for bfloat16
                  # Torch has no support for uint above uint8
                  return
  
      kw = {}
      for i, (array, dtype) in enumerate(zip(arrays, input_dtypes)):
          kw[f"x{i}"] = np.asarray(array, dtype=dtype)
      test_flags.num_positional_args = len(kw)
      helpers.test_function(
          input_dtypes=input_dtypes,
          test_flags=test_flags,
          backend_to_test=backend_fw,
          on_device=on_device,
          fn_name=fn_name,
          **kw,
      )
  
imports: import numpy
  from hypothesis import strategies
  import typing
  import ivy
  import ivy_tests.test_ivy.helpers
  from ivy_tests.test_ivy.helpers import handle_test, BackendHandler
label_desc: Use image node_id 11 for calling a global function with example usage: test_broadcast_arrays() without return types