code: def _validate_and_tranform_x_and_y(x, y):
      r"""Ensure `x` and `y` have proper shape and transform/reshape them if
      required.
  
      Parameters
      ----------
      x : array_like, 1-D or 2-D
          If `x` is 1-D than it is assumed to be a vector of observations of a
          single random variable. If `x` is 2-D than the rows should be
          observations and the columns are treated as the components of a
          random vector, i.e., each column represents a different component of
          the random vector `x`.
      y : array_like, 1-D or 2-D
          Same as `x`, but only the number of observation has to match that of
          `x`. If `y` is 2-D note that the number of columns of `y` (i.e., the
          number of components in the random vector) does not need to match
          the number of columns in `x`.
  
      Returns
      -------
      x : array_like, 1-D or 2-D
      y : array_like, 1-D or 2-D
  
      Raises
      ------
      ValueError
          If `x` and `y` have a different number of observations.
  
      """
      x = np.asanyarray(x)
      y = np.asanyarray(y)
  
      if x.shape[0] != y.shape[0]:
          raise ValueError(
              "x and y must have the same number of observations (rows)."
          )
  
      if len(x.shape) == 1:
          x = x.reshape((x.shape[0], 1))
  
      if len(y.shape) == 1:
          y = y.reshape((y.shape[0], 1))
  
      return x, y
  
file_path: statsmodels/statsmodels/stats/dist_dependence_measures.py
imports: from collections import namedtuple
  import warnings
  import numpy
  from scipy.spatial.distance import pdist, squareform
  from scipy.stats import norm
  from statsmodels.tools.sm_exceptions import HypothesisTestWarning
label_desc: Use image node_id 2 for calling a global function with example usage: _validate_and_tranform_x_and_y(x, y) and returns: x,  y