code: def _pairwise_distance_euclid(X, Y=None, squared=False):
      """Pairwise Euclidean distance matrix.
  
      Compute the matrix of Euclidean distances between pairs of elements of X
      and Y.
  
      Parameters
      ----------
      X : ndarray, shape (n_matrices_X, n, n)
          First set of matrices.
      Y : None | ndarray, shape (n_matrices_Y, n, n), default=None
          Second set of matrices. If None, Y is set to X.
      squared : bool, default False
          Return squared distances.
  
      Returns
      -------
      dist : ndarray, shape (n_matrices_X, n_matrices_X) or (n_matrices_X, \
              n_matrices_Y)
          Euclidean distances between pairs of elements of X if Y is None, or
          between elements of X and Y.
  
      See Also
      --------
      pairwise_distance
      distance_euclid
      """
      if Y is None:
          dist = euclidean_distances(
              X.reshape(len(X), -1), squared=squared
          )
      else:
          dist = euclidean_distances(
              X.reshape(len(X), -1),
              Y.reshape(len(Y), -1),
              squared=squared,
          )
      return dist
  
imports: import numpy
  from scipy.linalg import eigvalsh, solve
  from sklearn.metrics import euclidean_distances
  from .base import logm, sqrtm, invsqrtm
label_desc: Use image node_id 14 for calling a global function with example usage: _pairwise_distance_euclid(X, Y, squared) and returns: dist