code: def test_torch_outputs_to_frontend_arrays(
      dtype_and_x,
      dtype,
      generate_type,
      inplace,
      backend_fw,
  ):
      x_dtype, x = dtype_and_x
  
      ivy.set_backend(backend_fw)
  
      x = ivy.array(x[0], dtype=x_dtype[0])
      if generate_type == "frontend":
          x = Tensor(x)
      elif generate_type == "native":
          x = x.data
  
      if not len(x.shape):
          scalar_x = ivy.to_scalar(
              x.ivy_array if isinstance(x, Tensor) else x
          )
          outputs_to_frontend_arrays(_fn)(
              scalar_x, scalar_x, check_default=True, dtype=dtype
          )
          outputs_to_frontend_arrays(_fn)(
              scalar_x, x, check_default=True, dtype=dtype
          )
      output = outputs_to_frontend_arrays(_fn)(
          x, check_default=True, dtype=dtype, inplace=inplace
      )
      assert isinstance(output, Tensor)
      if inplace:
          if generate_type == "frontend":
              assert x is output
          elif generate_type == "native":
              assert x is output.ivy_array.data
          else:
              assert x is output.ivy_array
      else:
          assert ivy.as_ivy_dtype(x.dtype) == ivy.as_ivy_dtype(
              output.dtype
          )
          if generate_type == "frontend":
              assert ivy.all(x.ivy_array == output.ivy_array)
          elif generate_type == "native":
              assert ivy.all(x == output.ivy_array.data)
          else:
              assert ivy.all(x == 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_torch/test_func_wrapper.py
imports: from hypothesis import given, strategies
  import ivy
  import ivy_tests.test_ivy.helpers
  from ivy.functional.frontends.torch.func_wrapper import inputs_to_ivy_arrays, outputs_to_frontend_arrays, to_ivy_arrays_and_back, numpy_to_torch_style_args
  from ivy.functional.frontends.torch.tensor import Tensor
  import ivy.functional.frontends.torch
label_desc: Use image node_id 5 for calling a global function with example usage: test_torch_outputs_to_frontend_arrays(dtype_and_x, dtype, generate_type, inplace, backend_fw) without return types