code: def _get_tuning_df(self, run, params=None):
      exp_id = _get_experiment_id()
      primary_metric_tag = f"metrics.{self.primary_metric}"
      order_str = (
          "DESC"
          if self.evaluation_metrics_greater_is_better[
              self.primary_metric
          ]
          else "ASC"
      )
      tuning_runs = mlflow.search_runs(
          [exp_id],
          filter_string=f"tags.mlflow.parentRunId like '{run.info.run_id}'",
          order_by=[
              f"{primary_metric_tag} {order_str}",
              "attribute.start_time ASC",
          ],
      )
      if params:
          params = [f"params.{param}" for param in params]
          tuning_runs = tuning_runs.filter(
              [f"metrics.{self.primary_metric}", *params]
          )
      else:
          tuning_runs = tuning_runs.filter(
              [f"metrics.{self.primary_metric}"]
          )
      tuning_runs = tuning_runs.reset_index().rename(
          columns={
              "index": "Model Rank",
              primary_metric_tag: self.primary_metric,
          }
      )
      tuning_runs["Model Rank"] += 1
      return tuning_runs.head(10)
  
imports: import datetime
  import importlib
  import logging
  import os
  import re
  import shutil
  import sys
  import warnings
  import cloudpickle
  import yaml
  import mlflow
  from mlflow.entities import SourceType, ViewType
  from mlflow.environment_variables import MLFLOW_RECIPES_EXECUTION_TARGET_STEP_NAME
  from mlflow.exceptions import BAD_REQUEST, INVALID_PARAMETER_VALUE, MlflowException
  from mlflow.models import Model
  from mlflow.recipes.artifacts import DataframeArtifact, HyperParametersArtifact, ModelArtifact, RunArtifact
  from mlflow.recipes.cards import BaseCard
  from mlflow.recipes.step import BaseStep, StepClass
  from mlflow.recipes.utils.execution import get_step_output_path
  from mlflow.recipes.utils.metrics import _get_builtin_metrics, _get_custom_metrics, _get_error_fn, _get_extended_task, _get_model_type_from_template, _get_primary_metric, _load_custom_metrics, transform_multiclass_metrics_dict
  from mlflow.recipes.utils.step import get_merged_eval_metrics, get_pandas_data_profiles, validate_classification_config
  from mlflow.recipes.utils.tracking import TrackingConfig, apply_recipe_tracking_config, get_recipe_tracking_config, get_run_tags_env_vars, log_code_snapshot
  from mlflow.recipes.utils.wrapped_recipe_model import WrappedRecipeModel
  from mlflow.tracking import MlflowClient
  from mlflow.tracking.fluent import _get_experiment_id
  from mlflow.utils.databricks_utils import get_databricks_env_vars, get_databricks_run_url
  from mlflow.utils.file_utils import TempDir
  from mlflow.utils.mlflow_tags import MLFLOW_RECIPE_PROFILE_NAME, MLFLOW_RECIPE_STEP_NAME, MLFLOW_RECIPE_TEMPLATE_NAME, MLFLOW_SOURCE_TYPE
  from mlflow.utils.string_utils import strip_prefix
label_desc: Use image node_id 13 for calling the TrainStep obj's underlying member method code with example usage: obj._get_tuning_df(run, params) and returns: tuning_runs