code: def test_predict_dataset_complete_display_predict_with_progress_bar_coloring(
      self,
  ):
      x = torch.rand(ModelFittingTestCase.evaluate_dataset_len, 1)
      # we use the same color for all components for simplicity
      coloring = {
          "text_color": "WHITE",
          "ratio_color": "WHITE",
          "metric_value_color": "WHITE",
          "time_color": "WHITE",
          "progress_bar_color": "WHITE",
      }
  
      self.model.predict_dataset(
          x,
          verbose=True,
          progress_options=dict(coloring=coloring, progress_bar=True),
      )
  
      # We split per step update
      steps_update = self.test_out.getvalue().strip().split("\r")
  
      # last print update templating different
      last_print_regex = (
          r".*\[37mPrediction steps:.*"
          + ModelFittingTestCaseProgress.TIME_REGEX
      )
      self.assertRegex(steps_update[-1], last_print_regex)
  
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 45 for calling the ModelFittingTestCaseProgress obj's underlying member method code with example usage: obj.test_predict_dataset_complete_display_predict_with_progress_bar_coloring() without return types