code: def test_torch_to_ivy_arrays_and_back(dtype_and_x, dtype, backend_fw):
      x_dtype, x = dtype_and_x
  
      ivy.set_backend(backend_fw)
  
      # 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)
          to_ivy_arrays_and_back(_fn)(
              scalar_input_ivy,
              scalar_input_ivy,
              check_default=True,
              dtype=dtype,
          )
          to_ivy_arrays_and_back(_fn)(
              scalar_input_ivy,
              input_ivy,
              check_default=True,
              dtype=dtype,
          )
      output = to_ivy_arrays_and_back(_fn)(
          input_ivy, check_default=True, dtype=dtype
      )
      assert isinstance(output, Tensor)
      assert str(input_ivy.dtype) == str(output.dtype)
      assert ivy.all(input_ivy == output.ivy_array)
  
      # check for native array
      input_native = ivy.native_array(input_ivy)
      if not len(input_native.shape):
          scalar_input_native = ivy.to_scalar(input_native)
          to_ivy_arrays_and_back(_fn)(
              scalar_input_native,
              scalar_input_native,
              check_default=True,
              dtype=dtype,
          )
          to_ivy_arrays_and_back(_fn)(
              scalar_input_native,
              input_native,
              check_default=True,
              dtype=dtype,
          )
      output = to_ivy_arrays_and_back(_fn)(
          input_native, check_default=True, dtype=dtype
      )
      assert isinstance(output, Tensor)
      assert ivy.as_ivy_dtype(input_native.dtype) == str(output.dtype)
      assert ivy.all(input_native == output.ivy_array.data)
  
      # check for frontend array
      input_frontend = Tensor(x[0])
      input_frontend.ivy_array = input_ivy
      if not len(input_frontend.shape):
          scalar_input_front = inputs_to_ivy_arrays(ivy.to_scalar)(
              input_frontend
          )
          to_ivy_arrays_and_back(_fn)(
              scalar_input_front,
              scalar_input_front,
              check_default=True,
              dtype=dtype,
          )
          to_ivy_arrays_and_back(_fn)(
              scalar_input_front,
              input_frontend,
              check_default=True,
              dtype=dtype,
          )
      output = to_ivy_arrays_and_back(_fn)(
          input_frontend, check_default=True, dtype=dtype
      )
      assert isinstance(output, Tensor)
      assert input_frontend.dtype == output.dtype
      assert ivy.all(input_frontend.ivy_array == 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 6 for calling a global function with example usage: test_torch_to_ivy_arrays_and_back(dtype_and_x, dtype, backend_fw) without return types