code: def _label_encoded_fitted_estimator(
      self, estimator, X_train, y_train
  ):
      from sklearn.preprocessing import LabelEncoder
  
      label_encoder = None
      target_column_class_labels = None
      encoded_y_train = y_train
      if "classification" in self.recipe:
          label_encoder = LabelEncoder()
          label_encoder.fit(y_train)
          target_column_class_labels = label_encoder.classes_
          import pandas as pd
  
          encoded_y_train = pd.Series(label_encoder.transform(y_train))
  
      def inverse_label_encoder(predicted_output):
          if not label_encoder:
              return predicted_output
  
          return label_encoder.inverse_transform(predicted_output)
  
      estimator.fit(X_train, encoded_y_train)
      original_predict = estimator.predict
  
      def wrapped_predict(*args, **kwargs):
          return inverse_label_encoder(
              original_predict(*args, **kwargs)
          )
  
      estimator.predict = wrapped_predict
  
      return estimator, {
          "target_column_class_labels": target_column_class_labels
      }
  
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 7 for calling the TrainStep obj's underlying member method code with example usage: obj._label_encoded_fitted_estimator(estimator, X_train, y_train) and returns: estimator,  dict, label_encoder, inverse_label_encoder, predicted_output