code: def from_recipe_config(cls, recipe_config, recipe_root):
      step_config = {}
      if recipe_config.get("steps", {}).get("train", {}) is not None:
          step_config.update(
              recipe_config.get("steps", {}).get("train", {})
          )
      if recipe_config.get("custom_metrics") is not None:
          step_config["custom_metrics"] = recipe_config[
              "custom_metrics"
          ]
      if recipe_config.get("primary_metric") is not None:
          step_config["primary_metric"] = recipe_config[
              "primary_metric"
          ]
      step_config["recipe"] = recipe_config.get("recipe")
      step_config["profile"] = recipe_config.get("profile")
      step_config["target_col"] = recipe_config.get("target_col")
      if "positive_class" in recipe_config:
          step_config["positive_class"] = recipe_config.get(
              "positive_class"
          )
      step_config.update(
          get_recipe_tracking_config(
              recipe_root_path=recipe_root,
              recipe_config=recipe_config,
          ).to_dict()
      )
      return cls(step_config, recipe_root, recipe_config=recipe_config)
  
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 15 for calling the TrainStep obj's underlying member method code with example usage: obj.from_recipe_config(cls, recipe_config, recipe_root) and returns: cls