code: def test_predict_dataset_with_user_coloring_invalid(self):
      x = torch.rand(ModelFittingTestCase.evaluate_dataset_len, 1)
  
      with self.assertRaises(KeyError):
          self.model.predict_dataset(
              x,
              batch_size=ModelFittingTestCase.batch_size,
              progress_options=dict(
                  coloring={"invalid_name": "A COLOR"}
              ),
          )
  
imports: from unittest import skipIf
  import torch
  import torch.nn
  from torch.utils.data import DataLoader
  from poutyne import Model, TensorDataset
  from tests.framework.model.base import ModelFittingTestCase
  from tests.framework.tools import SomeConstantMetric, SomeDataGeneratorUsingStopIteration, repeat_batch_metric, repeat_batch_metric_value, some_batch_metric_1, some_batch_metric_2, some_constant_metric_value, some_data_tensor_generator, some_metric_1_value, some_metric_2_value
label_desc: Use image node_id 39 for calling the ModelFittingTestCaseProgress obj's underlying member method code with example usage: obj.test_predict_dataset_with_user_coloring_invalid() without return types