code: def test_numpy_outputs_to_frontend_arrays(
      dtype_and_x, dtype, backend_fw
  ):
      ivy.set_backend(backend_fw)
      x_dtype, x = dtype_and_x
  
      # check for ivy array
      input_ivy = ivy.array(x[0], dtype=x_dtype[0])
      if not len(input_ivy.shape):
          scalar_input_ivy = ivy.to_scalar(input_ivy)
          outputs_to_frontend_arrays(_fn)(
              scalar_input_ivy,
              scalar_input_ivy,
              check_default=True,
              dtype=dtype,
          )
          outputs_to_frontend_arrays(_fn)(
              scalar_input_ivy,
              input_ivy,
              check_default=True,
              dtype=dtype,
          )
      output = outputs_to_frontend_arrays(_fn)(
          input_ivy, check_default=True, dtype=dtype
      )
      assert isinstance(output, ndarray)
      assert input_ivy.dtype == output.ivy_array.dtype
      assert ivy.all(input_ivy == output.ivy_array)
  
      assert (
          ivy.default_float_dtype_stack
          == ivy.default_int_dtype_stack
          == []
      )
      ivy.previous_backend()
  
file_path: ivy/ivy_tests/test_ivy/test_frontends/test_numpy/test_func_wrapper.py
imports: from hypothesis import given, strategies
  import platform
  import ivy
  import ivy_tests.test_ivy.helpers
  from ivy.functional.frontends.numpy.func_wrapper import inputs_to_ivy_arrays, outputs_to_frontend_arrays, to_ivy_arrays_and_back, handle_numpy_dtype, from_zero_dim_arrays_to_scalar
  from ivy.functional.frontends.numpy.ndarray import ndarray
  import ivy.functional.frontends.numpy
label_desc: Use image node_id 8 for calling a global function with example usage: test_numpy_outputs_to_frontend_arrays(dtype_and_x, dtype, backend_fw) without return types