code: def test_torch_where(
      *,
      broadcastables,
      only_cond,
      frontend,
      test_flags,
      fn_tree,
      backend_fw,
      on_device,
  ):
      dtypes, arrays = broadcastables
  
      if only_cond:
          helpers.test_frontend_function(
              input_dtypes=[dtypes[0]],
              backend_to_test=backend_fw,
              frontend=frontend,
              test_flags=test_flags,
              fn_tree=fn_tree,
              on_device=on_device,
              condition=arrays[0],
          )
  
      else:
          helpers.test_frontend_function(
              input_dtypes=dtypes,
              backend_to_test=backend_fw,
              frontend=frontend,
              test_flags=test_flags,
              fn_tree=fn_tree,
              on_device=on_device,
              condition=arrays[0],
              input=arrays[1],
              other=arrays[2],
          )
  
imports: import random
  from hypothesis import strategies
  import math
  import ivy
  import ivy_tests.test_ivy.helpers
  import ivy_tests.test_ivy.helpers.globals
  from ivy_tests.test_ivy.helpers import handle_frontend_test
  from ivy_tests.test_ivy.test_functional.test_core.test_manipulation import _get_splits
  from ivy_tests.test_ivy.helpers.hypothesis_helpers.general_helpers import two_broadcastable_shapes
label_desc: Use image node_id 53 for calling a global function with example usage: test_torch_where() without return types