code: def test_torch_inputs_to_ivy_arrays(dtype_and_x, backend_fw):
      x_dtype, x = dtype_and_x
  
      ivy.set_backend(backend=backend_fw)
  
      # check for ivy array
      input_ivy = ivy.array(x[0], dtype=x_dtype[0])
      output = inputs_to_ivy_arrays(_fn)(input_ivy)
      assert isinstance(output, ivy.Array)
      assert input_ivy.dtype == output.dtype
      assert ivy.all(input_ivy == output)
  
      # check for native array
      input_native = ivy.native_array(input_ivy)
      output = inputs_to_ivy_arrays(_fn)(input_native)
      assert isinstance(output, ivy.Array)
      assert ivy.as_ivy_dtype(input_native.dtype) == str(output.dtype)
      assert ivy.all(input_native == output.data)
  
      # check for frontend array
      input_frontend = Tensor(x[0])
      input_frontend.ivy_array = input_ivy
      output = inputs_to_ivy_arrays(_fn)(input_frontend)
      assert isinstance(output, ivy.Array)
      assert str(input_frontend.dtype) == str(output.dtype)
      assert ivy.all(input_frontend.ivy_array == output)
  
      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 3 for calling a global function with example usage: test_torch_inputs_to_ivy_arrays(dtype_and_x, backend_fw) without return types