code: def _validate_and_apply_step_config(self):
      self.target_col = self.step_config.get("target_col")
      self.positive_class = self.step_config.get("positive_class")
      if self.target_col is None:
          raise MlflowException(
              "Missing target_col config in recipe config.",
              error_code=INVALID_PARAMETER_VALUE,
          )
      if "using" in self.step_config:
          if self.step_config["using"] not in ["custom"]:
              raise MlflowException(
                  f"Invalid transform step configuration value {self.step_config['using']} for "
                  f"key 'using'. Supported values are: ['custom']",
                  error_code=INVALID_PARAMETER_VALUE,
              )
      else:
          self.step_config["using"] = "custom"
      self.run_end_time = None
      self.execution_duration = None
      self.skip_data_profiling = self.step_config.get(
          "skip_data_profiling", False
      )
  
imports: import importlib
  import logging
  import os
  import sys
  import time
  import cloudpickle
  from packaging.version import Version
  from mlflow.exceptions import INVALID_PARAMETER_VALUE, MlflowException
  from mlflow.recipes.artifacts import DataframeArtifact, TransformerArtifact
  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.step import get_pandas_data_profiles, validate_classification_config
  from mlflow.recipes.utils.tracking import TrackingConfig, get_recipe_tracking_config
label_desc: Use image node_id 2 for calling the TransformStep obj's underlying member method code with example usage: obj._validate_and_apply_step_config() without return types