code: def _check_X_y(
      X,
      y,
      dtype="numeric",
      accept_sparse=False,
      order=None,
      copy=False,
      force_all_finite=True,
      ensure_2d=True,
      accept_large_sparse=True,
      y_numeric=False,
      accept_2d_y=False,
  ):
      if y is None:
          raise ValueError("y cannot be None")
  
      X = _check_array(
          X,
          accept_sparse=accept_sparse,
          dtype=dtype,
          order=order,
          copy=copy,
          force_all_finite=force_all_finite,
          ensure_2d=ensure_2d,
          accept_large_sparse=accept_large_sparse,
      )
  
      if not accept_2d_y:
          y = _column_or_1d(y, warn=True)
      if y_numeric and y.dtype.kind == "O":
          y = y.astype(np.float64)
      _assert_all_finite(y)
  
      lengths = [X.shape[0], y.shape[0]]
      uniques = np.unique(lengths)
      if len(uniques) > 1:
          raise ValueError(
              "Found input variables with inconsistent numbers of"
              " samples: %r" % [int(length) for length in lengths]
          )
  
      return X, y
  
imports: import warnings
  from collections.abc import Sequence
  from numbers import Integral
  import numpy
  from scipy import sparse
  from sklearn.preprocessing import LabelEncoder
  from sklearn.utils.validation import check_array
  from daal4py.sklearn.utils.validation import _assert_all_finite
label_desc: Use image node_id 7 for calling a global function with example usage: _check_X_y(X, y, dtype, accept_sparse, order, copy, force_all_finite, ensure_2d, accept_large_sparse, y_numeric, accept_2d_y) and returns: X,  y