code: def test_0_predict_file_flow(self):
      if QClassic is None:
          print(
              "Skipping this test since the system for the quantum are not installed"
          )
          return
  
      df = pd.read_csv("tests/data/hdi.csv")[:500]
      target = "Development Index"
  
      pdef = ProblemDefinition.from_dict(
          {"target": target, "time_aim": 80}
      )
      json_ai = json_ai_from_problem(df, problem_definition=pdef)
  
      neural_args = json_ai.model["args"]["submodels"][0]["args"]
      neural_args["target_encoder"] = "$encoders[self.target]"
      neural_args["target"] = "$target"
      neural_args["dtype_dict"] = "$dtype_dict"
      neural_args["input_cols"] = "$input_cols"
      neural_args[
          "timeseries_settings"
      ] = "$problem_definition.timeseries_settings"
      neural_args["net"] = '"DefaultNet"'
  
      json_ai.model["args"]["submodels"] = [
          {"module": "lightwood.mixer.QClassic", "args": neural_args}
      ]
  
      code = code_from_json_ai(json_ai)
      predictor = predictor_from_code(code)
  
      predictor.learn(df)
      predictions = predictor.predict(df)
      return
  
      # sanity checks
      self.assertTrue(
          accuracy_score(df[target], predictions["prediction"]) > 0.5
      )
      self.assertTrue(
          all([0 <= p <= 1 for p in predictions["confidence"]])
      )
  
file_path: lightwood/tests/integration/basic/test_qclassic.py
imports: import unittest
  import pandas
  from sklearn.metrics import accuracy_score
  from lightwood.api.high_level import ProblemDefinition, json_ai_from_problem
  from lightwood.api.high_level import code_from_json_ai, predictor_from_code
  from lightwood.mixer import QClassic
label_desc: Use image node_id 1 for calling the TestBasic obj's underlying member method code with example usage: obj.test_0_predict_file_flow() without return types