code: def _run(self, output_directory):
      import pandas as pd
  
      run_start_time = time.time()
  
      train_data_path = get_step_output_path(
          recipe_root_path=self.recipe_root,
          step_name="split",
          relative_path="train.parquet",
      )
      train_df = pd.read_parquet(train_data_path)
      validate_classification_config(
          self.task, self.positive_class, train_df, self.target_col
      )
  
      validation_data_path = get_step_output_path(
          recipe_root_path=self.recipe_root,
          step_name="split",
          relative_path="validation.parquet",
      )
      validation_df = pd.read_parquet(validation_data_path)
  
      sys.path.append(self.recipe_root)
  
      def get_identity_transformer():
          from sklearn.pipeline import Pipeline
          from sklearn.preprocessing import FunctionTransformer
  
          return Pipeline(steps=[("identity", FunctionTransformer())])
  
      if (
          "transformer_method" not in self.step_config
          and self.step_config["using"] == "custom"
      ):
          raise MlflowException(
              "Missing 'transformer_method' configuration in the transform step, "
              "which is using 'custom'.",
              error_code=INVALID_PARAMETER_VALUE,
          )
      method_config = self.step_config.get("transformer_method")
      transformer = None
      if method_config and self.step_config["using"] == "custom":
          transformer_fn = getattr(
              importlib.import_module(
                  _USER_DEFINED_TRANSFORM_STEP_MODULE
              ),
              method_config,
          )
          transformer = _validate_user_code_output(transformer_fn)
      transformer = (
          transformer if transformer else get_identity_transformer()
      )
      transformer.fit(
          train_df.drop(columns=[self.target_col]),
          train_df[self.target_col],
      )
  
      def transform_dataset(dataset):
          features = dataset.drop(columns=[self.target_col])
          transformed_features = transformer.transform(features)
          if not isinstance(transformed_features, pd.DataFrame):
              num_features = transformed_features.shape[1]
              columns = _get_output_feature_names(
                  transformer, num_features, features.columns
              )
              transformed_features = pd.DataFrame(
                  transformed_features, columns=columns
              )
          transformed_features[self.target_col] = dataset[
              self.target_col
          ].values
          return transformed_features
  
      train_transformed = transform_dataset(train_df)
      validation_transformed = transform_dataset(validation_df)
  
      with open(
          os.path.join(output_directory, "transformer.pkl"), "wb"
      ) as f:
          cloudpickle.dump(transformer, f)
  
      train_transformed.to_parquet(
          os.path.join(
              output_directory, "transformed_training_data.parquet"
          )
      )
      validation_transformed.to_parquet(
          os.path.join(
              output_directory, "transformed_validation_data.parquet"
          )
      )
  
      self.run_end_time = time.time()
      self.execution_duration = self.run_end_time - run_start_time
  
      return self._build_profiles_and_card(
          train_df, train_transformed, transformer
      )
  
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 3 for calling the TransformStep obj's underlying member method code with example usage: obj._run(output_directory) and returns: self, Pipeline, transformed_features