code: def validate_classification_config(
      task: str,
      positive_class: str,
      input_df: pd.DataFrame,
      target_col: str,
  ):
      """
  
      :param task:
      :param positive_class:
      :param input_df:
      :param target_col:
      """
      if task == "classification":
          classes = np.unique(input_df[target_col])
          num_classes = len(classes)
          if num_classes <= 1:
              raise MlflowException(
                  f"Classification tasks require at least two tasks. "
                  f"Your dataset contains {num_classes}."
              )
          elif positive_class is None and num_classes == 2:
              raise MlflowException(
                  "`positive_class` must be specified for classification/v1 recipes.",
                  error_code=INVALID_PARAMETER_VALUE,
              )
  
file_path: mlflow/mlflow/recipes/utils/step.py
imports: import logging
  import os
  import shutil
  import subprocess
  from typing import Dict, Iterable, List, Optional, Tuple
  import numpy
  import pandas
  from mlflow.exceptions import BAD_REQUEST, INVALID_PARAMETER_VALUE, MlflowException
  from mlflow.recipes.cards import pandas_renderer
  from mlflow.utils.databricks_utils import get_databricks_runtime, is_in_databricks_runtime, is_running_in_ipython_environment
label_desc: Use image node_id 6 for calling a global function with example usage: validate_classification_config(task, positive_class, input_df, target_col) without return types