code: def test_evaluate_with_np_array_multi_input(self):
      x1 = np.random.rand(
          ModelMultiInputTest.evaluate_dataset_len, 1
      ).astype(np.float32)
      x2 = np.random.rand(
          ModelMultiInputTest.evaluate_dataset_len, 1
      ).astype(np.float32)
      x = (x1, x2)
      y = np.random.rand(
          ModelMultiInputTest.evaluate_dataset_len, 1
      ).astype(np.float32)
      loss, pred_y = self.model.evaluate(
          x,
          y,
          batch_size=ModelMultiInputTest.batch_size,
          return_pred=True,
      )
      self.assertEqual(type(loss), float)
      self.assertEqual(
          pred_y.shape, (ModelMultiInputTest.evaluate_dataset_len, 1)
      )
  
imports: import numpy
  import torch
  import torch.nn
  from torch.utils.data import DataLoader
  from poutyne import Model, TensorDataset
  from tests.framework.model.base import ModelFittingTestCase, MultiIOModel
label_desc: Use image node_id 9 for calling the ModelMultiInputTest obj's underlying member method code with example usage: obj.test_evaluate_with_np_array_multi_input() without return types