code: def _daal_validate_data(
      self,
      X,
      y=None,
      reset=True,
      validate_separately=False,
      **check_params,
  ):
      """Validate input data and set or check the `n_features_in_` attribute.
  
      Parameters
      ----------
      X : {array-like, sparse matrix, dataframe} of shape \
              (n_samples, n_features)
          The input samples.
      y : array-like of shape (n_samples,), default=None
          The targets. If None, `check_array` is called on `X` and
          `check_X_y` is called otherwise.
      reset : bool, default=True
          Whether to reset the `n_features_in_` attribute.
          If False, the input will be checked for consistency with data
          provided when reset was last True.
      validate_separately : False or tuple of dicts, default=False
          Only used if y is not None.
          If False, call validate_X_y(). Else, it must be a tuple of kwargs
          to be used for calling check_array() on X and y respectively.
      **check_params : kwargs
          Parameters passed to :func:`sklearn.utils.check_array` or
          :func:`sklearn.utils.check_X_y`. Ignored if validate_separately
          is not False.
  
      Returns
      -------
      out : {ndarray, sparse matrix} or tuple of these
          The validated input. A tuple is returned if `y` is not None.
      """
  
      if y is None:
          if self._get_tags()["requires_y"]:
              raise ValueError(
                  f"This {self.__class__.__name__} estimator "
                  f"requires y to be passed, but the target y is None."
              )
          X = _daal_check_array(X, **check_params)
          out = X
      else:
          if validate_separately:
              # We need this because some estimators validate X and y
              # separately, and in general, separately calling check_array()
              # on X and y isn't equivalent to just calling check_X_y()
              # :(
              check_X_params, check_y_params = validate_separately
              X = _daal_check_array(X, **check_X_params)
              y = _daal_check_array(y, **check_y_params)
          else:
              X, y = _daal_check_X_y(X, y, **check_params)
          out = X, y
  
      if check_params.get("ensure_2d", True):
          self._check_n_features(X, reset=reset)
      return out
  
label_desc: Use image node_id 1 for calling a global function with example usage: _daal_validate_data(X, y, reset, validate_separately) and returns: out