code: def distance_euclid(A, B, squared=False):
      r"""Euclidean distance between matrices.
  
      The Euclidean distance between two matrices :math:`\mathbf{A}` and
      :math:`\mathbf{B}` is defined as the Frobenius norm of the difference of
      the two matrices:
  
      .. math::
          d(\mathbf{A},\mathbf{B}) = \Vert \mathbf{A} - \mathbf{B} \Vert_F
  
      Parameters
      ----------
      A : ndarray, shape (..., n, m)
          First matrices, at least 2D ndarray.
      B : ndarray, shape (..., n, m)
          Second matrices, same dimensions as A.
      squared : bool, default False
          Return squared distance.
  
          .. versionadded:: 0.5
  
      Returns
      -------
      d : float or ndarray, shape (...,)
          Euclidean distance between A and B.
  
      See Also
      --------
      distance
      """
      _check_inputs(A, B)
      d = np.linalg.norm(A - B, ord="fro", axis=(-2, -1))
      return d**2 if squared else d
  
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 3 for calling a global function with example usage: distance_euclid(A, B, squared) and returns: unknown