code: def test_numpy_nan_to_num(
      dtype_and_x,
      copy,
      nan,
      fn_tree,
      frontend,
      test_flags,
      backend_fw,
      on_device,
      posinf,
      neginf,
  ):
      input_dtype, x = dtype_and_x
      # to avoid overflow errors of tf as you can't easily create a tensor
      # close to tf.dtype.max or tf.dtype.min, we need to assume that
      if ivy.current_backend_str() == "tensorflow":
          assume(posinf is not None and neginf is not None)
      helpers.test_frontend_function(
          input_dtypes=input_dtype,
          backend_to_test=backend_fw,
          frontend=frontend,
          test_flags=test_flags,
          fn_tree=fn_tree,
          on_device=on_device,
          x=x[0],
          copy=copy,
          nan=nan,
          posinf=posinf,
          neginf=neginf,
      )
  
imports: from hypothesis import assume, strategies
  import numpy
  import ivy_tests.test_ivy.helpers
  import ivy_tests.test_ivy.test_frontends.test_numpy.helpers
  from ivy_tests.test_ivy.helpers import handle_frontend_test
  import ivy
label_desc: Use image node_id 12 for calling a global function with example usage: test_numpy_nan_to_num(dtype_and_x, copy, nan, fn_tree, frontend, test_flags, backend_fw, on_device, posinf, neginf) without return types