/*expected_max_outputs=*/1, status.get());
  EXPECT_NE(TF_GetCode(status.get()), TF_CANCELLED);
  EXPECT_EQ(TF_GetCode(status.get()), TF_INVALID_ARGUMENT);
  EXPECT_THAT(TF_Message(status.get()), HasSubstr("assertion failed"));

  // Note that future collectives with the same context do not work at the

          " outputs for TPUReplicatedOutput, but got ", expected_outputs));
      TF_SetStatus(status, TF_INVALID_ARGUMENT, message.c_str());
      return result;
    }
    if (absl::holds_alternative<TFE_TensorHandle*>(inputs[0])) {
      TF_SetStatus(status, TF_INVALID_ARGUMENT,
                   "Expected the input to "
                   "TPUReplicatedOutput to be a parallel tensor (placed on the "

    return;
  } else if (S_ISDIR(st.st_mode)) {
    TF_SetStatus(status, TF_FAILED_PRECONDITION, "source path is a directory");
    return;
  }

  // Do the actual rename. Here both arguments are filenames.
  if (rename(src, dst) != 0)
    TF_SetStatusFromIOError(status, errno, dst);
  else
    TF_SetStatus(status, TF_OK, "");
}

  // 1) nullptr indicates tensor is compact and row-majored.
  // 2) fill in the strides array as the real case for compact row-major data.
  // Here we choose option 2, since some frameworks didn't handle the strides
  // argument properly.
  dlm_tensor->dl_tensor.strides = stride_arr->data();

  dlm_tensor->dl_tensor.byte_offset =
      0;  // TF doesn't handle the strides and byte_offsets here
  return static_cast<void*>(dlm_tensor);
}

    TensorHandlePtr combined_value = CreatePerDeviceValues(
        context.get(), components, device_name, status.get());
    ASSERT_TRUE(TF_GetCode(status.get()) == TF_INVALID_ARGUMENT)
        << TF_Message(status.get());
  }

  {
    // Try to extract the wrong number of components from a parallel tensor
    std::array<TFE_TensorHandle*, 1> correct_components{value_one.get()};

  Status s = RunModel(RecordOperationWithNullGradientFunctionModel, ctx.get(),
                      {x.get()}, absl::MakeSpan(outputs),
                      /*use_function=*/!std::get<2>(GetParam()));
  ASSERT_EQ(error::INVALID_ARGUMENT, s.code());
  ASSERT_EQ(
      "Provided null gradient_function for 'Neg'.\nIf the intent is to treat "
      "this op as non-differentiable consider using RegisterNotDifferentiable "

void TF_GraphCopyFunction(TF_Graph* g, const TF_Function* func,
                          const TF_Function* grad, TF_Status* status) {
  if (func == nullptr) {
    status->status = InvalidArgument(
        "'func' argument to TF_GraphCopyFunction cannot be null");
    return;
  }

  tensorflow::mutex_lock l(g->mu);
  status->status = g->graph.AddFunctionDef(func->record->fdef(),

  DefineFunction("MyGrad2", &grad_func2);

  // func cannot be null
  TF_GraphCopyFunction(host_graph_, nullptr, func_, s_);
  EXPECT_EQ(TF_INVALID_ARGUMENT, TF_GetCode(s_));
  EXPECT_EQ(string("'func' argument to TF_GraphCopyFunction cannot be null"),
            string(TF_Message(s_)));

  // Cannot change gradient
  TF_GraphCopyFunction(host_graph_, func_, grad_func1, s_);

        } else {
          return TF_SetStatus(status, TF_INVALID_ARGUMENT, "Not a directory!");
        }
      };
  GcsFileStat stat;
  MaybeAppendSlash(&dir);
  gcs_file->stat_cache->LookupOrCompute(dir, &stat, compute_func, status);
  if (TF_GetCode(status) != TF_OK && TF_GetCode(status) != TF_INVALID_ARGUMENT)
    return false;
  if (TF_GetCode(status) == TF_INVALID_ARGUMENT) {
    TF_SetStatus(status, TF_OK, "");
    return false;

  TF_DeleteFunction(funcs[0]);
}

TEST_F(CApiExperimentalFunctionTest, EmptyGraphRemoveNonExistentFunction) {
  TF_GraphRemoveFunction(func_graph_, "wrong_name", s_);
  EXPECT_EQ(TF_INVALID_ARGUMENT, TF_GetCode(s_));
  EXPECT_EQ(string("Tried to remove non-existent function 'wrong_name'."),
            string(TF_Message(s_)));
}

TEST_F(CApiExperimentalFunctionTest, GraphRemoveNonExistentFunction) {