code: def _ensure_2d(x, ndarray=False):
      """
  
      Parameters
      ----------
      x : ndarray, Series, DataFrame or None
          Input to verify dimensions, and to transform as necesary
      ndarray : bool
          Flag indicating whether to always return a NumPy array. Setting False
          will return an pandas DataFrame when the input is a Series or a
          DataFrame.
  
      Returns
      -------
      out : ndarray, DataFrame or None
          array or DataFrame with 2 dimensiona.  One dimensional arrays are
          returned as nobs by 1. None is returned if x is None.
      names : list of str or None
          list containing variables names when the input is a pandas datatype.
          Returns None if the input is an ndarray.
  
      Notes
      -----
      Accepts None for simplicity
      """
      if x is None:
          return x
      is_pandas = _is_using_pandas(x, None)
      if x.ndim == 2:
          if is_pandas:
              return x, x.columns
          else:
              return x, None
      elif x.ndim > 2:
          raise ValueError("x mst be 1 or 2-dimensional.")
  
      name = x.name if is_pandas else None
      if ndarray:
          return np.asarray(x)[:, None], name
      else:
          return pd.DataFrame(x), name
  
imports: import numpy
  import pandas
  import scipy.linalg
  from statsmodels.tools.data import _is_using_pandas
  from statsmodels.tools.validation import array_like
label_desc: Use image node_id 15 for calling a global function with example usage: _ensure_2d(x, ndarray) and returns: x, np,  name, pd,  name, x,  x, x,  None