code: def evaluate(
      self,
      *,
      model,
      model_type,
      dataset,
      run_id,
      evaluator_config,
      baseline_model=None,
      **kwargs,
  ):
      self.model_type = model_type
      self.client = MlflowClient()
      self.dataset = dataset
      self.run_id = run_id
      self.X = dataset.features_data
      self.y = dataset.labels_data
      y_pred = (
          model.predict(self.X)
          if model is not None
          else self.dataset.predictions_data
      )
      eval_result = self._evaluate(y_pred, is_baseline_model=False)
  
      if not baseline_model:
          return eval_result
  
      y_pred_baseline = baseline_model.predict(self.X)
      baseline_model_eval_result = self._evaluate(
          y_pred_baseline, is_baseline_model=True
      )
      return EvaluationResult(
          metrics=eval_result.metrics,
          artifacts=eval_result.artifacts,
          baseline_model_metrics=baseline_model_eval_result.metrics,
      )
  
imports: import io
  import os
  import tempfile
  import pandas
  from PIL import Image
  from sklearn import metrics
  from mlflow import MlflowClient
  from mlflow.entities import Metric
  from mlflow.models.evaluation import EvaluationArtifact, EvaluationResult, ModelEvaluator
  from mlflow.models.evaluation.artifacts import ImageEvaluationArtifact
  from mlflow.tracking.artifact_utils import get_artifact_uri
  from mlflow.utils.time import get_current_time_millis
label_desc: Use image node_id 4 for calling the DummyEvaluator obj's underlying member method code with example usage: obj.evaluate() and returns: EvaluationResult, eval_result