code: def check_inputs(X, y, sample_weight=None, ensure_2d=True):
      """Input validation for debiasing algorithms.
  
      Checks all inputs for consistent length, validates shapes (optional for X),
      and returns an array of all ones if sample_weight is ``None``.
  
      Args:
          X (array-like): Input data.
          y (array-like, shape = (n_samples,)): Target values.
          sample_weight (array-like, optional): Sample weights.
          ensure_2d (bool, optional): Whether to raise a ValueError if X is not
              2D.
  
      Returns:
          tuple:
  
              * **X** (`array-like`) -- Validated X. Unchanged.
  
              * **y** (`array-like`) -- Validated y. Possibly converted to 1D if
                not a :class:`pandas.Series`.
              * **sample_weight** (`array-like`) -- Validated sample_weight. If no
                sample_weight is provided, returns a consistent-length array of
                ones.
      """
      if ensure_2d and X.ndim != 2:
          raise ValueError(
              "Expected X to be 2D, got ndim == {} instead.".format(
                  X.ndim
              )
          )
      if not isinstance(y, pd.Series):  # don't cast Series -> ndarray
          y = column_or_1d(y)
      if sample_weight is not None:
          sample_weight = column_or_1d(sample_weight)
      else:
          sample_weight = np.ones(X.shape[0])
      check_consistent_length(X, y, sample_weight)
      return X, y, sample_weight
  
imports: import numpy
  import pandas
  from pandas.core.dtypes.common import is_list_like
  from sklearn.utils import check_consistent_length
  from sklearn.utils.validation import column_or_1d
label_desc: Use image node_id 1 for calling a global function with example usage: check_inputs(X, y, sample_weight, ensure_2d) and returns: X,  y,  sample_weight