code: def convert_if_sparse(
      y: SUPPORTED_TARGET_TYPES,
  ) -> Union[np.ndarray, List, pd.DataFrame, pd.Series]:
      """If the labels `y` are sparse, it will convert it to its dense representation
  
      Parameters
      ----------
      y: {array-like, sparse matrix} of shape (n_samples,) or (n_samples, n_outputs)
          The labels to 'densify' if sparse
  
      Returns
      -------
      np.ndarray of shape (n_samples, ) or (n_samples, n_outputs)
      """
      if isinstance(y, spmatrix):
          y_ = y.toarray()
  
          # For sparse one dimensional data, y.toarray will return [[1], [2], [3], ...]
          # We need to flatten this before returning it
          if y_.shape[0] == 1:
              y_ = y_.flatten()
      else:
          y_ = y
  
      return y_
  
file_path: auto-sklearn/autosklearn/data/validation.py
imports: from typing import List, Optional, Tuple, Union
  import logging
  import numpy
  import pandas
  from scipy.sparse import spmatrix
  from sklearn.base import BaseEstimator
  from sklearn.exceptions import NotFittedError
  from autosklearn.data.feature_validator import SUPPORTED_FEAT_TYPES, FeatureValidator
  from autosklearn.data.target_validator import SUPPORTED_TARGET_TYPES, TargetValidator
  from autosklearn.util.logging_ import get_named_client_logger
label_desc: Use image node_id 1 for calling a global function with example usage: convert_if_sparse(y) and returns: y_