code: def test_tensorflow_to_ivy_arrays_and_back(dtype_and_x):
      x_dtype, x = dtype_and_x
  
      # check for ivy array
      input_ivy = ivy.array(x[0], dtype=x_dtype[0])
      output = to_ivy_arrays_and_back(_fn)(input_ivy)
      assert isinstance(output, EagerTensor)
      assert input_ivy.dtype == output.dtype.ivy_dtype
      assert ivy.all(input_ivy == output.ivy_array)
  
      # check for native array
      input_native = ivy.native_array(input_ivy)
      output = to_ivy_arrays_and_back(_fn)(input_native)
      assert isinstance(output, EagerTensor)
      assert (
          ivy.as_ivy_dtype(input_native.dtype) == output.dtype.ivy_dtype
      )
      assert ivy.all(input_native == output.ivy_array.data)
  
      # check for frontend array
      input_frontend = EagerTensor(x[0])
      output = to_ivy_arrays_and_back(_fn)(input_frontend)
      assert isinstance(output, EagerTensor)
      assert input_frontend.dtype == output.dtype
      assert ivy.all(input_frontend.ivy_array == output.ivy_array)
  
file_path: ivy/ivy_tests/test_ivy/test_frontends/test_tensorflow/test_func_wrapper.py
imports: from hypothesis import given, strategies
  import ivy
  import ivy_tests.test_ivy.helpers
  from ivy_tests.test_ivy.helpers import BackendHandler
  from ivy.functional.frontends.tensorflow.func_wrapper import outputs_to_frontend_arrays, to_ivy_arrays_and_back, handle_tf_dtype
  from ivy.functional.frontends.tensorflow.tensor import EagerTensor
  import ivy.functional.frontends.tensorflow
  import ivy.functional.frontends.numpy
label_desc: Use image node_id 6 for calling a global function with example usage: test_tensorflow_to_ivy_arrays_and_back(dtype_and_x) without return types