code: def kron_matrix_op(krons, m, op):
      r"""Apply op to krons and m in a way that reproduces ``op(kronecker(*krons), m)``
  
      Parameters
      ----------
      krons : list of square 2D array-like objects
          D square matrices :math:`[A_1, A_2, ..., A_D]` to be Kronecker'ed
          :math:`A = A_1 \otimes A_2 \otimes ... \otimes A_D`
          Product of column dimensions must be :math:`N`
      m : NxM array or 1D array (treated as Nx1)
          Object that krons act upon
  
      Returns
      -------
      numpy array
      """
  
      def flat_matrix_op(flat_mat, mat):
          Nmat = mat.shape[1]
          flat_shape = flat_mat.shape
          mat2 = flat_mat.reshape((Nmat, -1))
          return op(mat, mat2).T.reshape(flat_shape)
  
      def kron_vector_op(v):
          return reduce(flat_matrix_op, krons, v)
  
      if m.ndim == 1:
          m = m[:, None]  # Treat 1D array as Nx1 matrix
      if m.ndim != 2:  # Has not been tested otherwise
          raise ValueError(f"m must have ndim <= 2, not {m.ndim}")
      result = kron_vector_op(m)
      result_shape = result.shape
      return pt.reshape(result, (result_shape[1], result_shape[0])).T
  
imports: import sys
  import warnings
  from functools import partial, reduce
  import numpy
  import pytensor
  import pytensor.sparse
  import pytensor.tensor
  import pytensor.tensor.slinalg
  import scipy
  import scipy.sparse
  from pytensor.graph.basic import Apply
  from pytensor.graph.op import Op
  from pytensor.tensor import abs, and_, arccos, arccosh, arcsin, arcsinh, arctan, arctanh, broadcast_to, ceil, clip, concatenate, constant, cos, cosh, cumprod, cumsum, dot, eq, erf, erfc, erfcinv, erfinv, exp, flatten, floor, full, full_like, ge, gt, le, log, log1pexp, logaddexp, logsumexp, lt, matmul, max, maximum, mean, min, minimum, neq, ones, ones_like, or_, prod, sgn, sigmoid, sin, sinh, sqr, sqrt, stack, sum, switch, tan, tanh, where, zeros, zeros_like
  from pytensor.tensor.linalg import solve_triangular
  from pytensor.tensor.nlinalg import matrix_inverse
  from pytensor.tensor.special import log_softmax, softmax
  from scipy.linalg import block_diag
  from pymc.pytensorf import floatX, ix_, largest_common_dtype
label_desc: Use image node_id 3 for calling a global function with example usage: kron_matrix_op(krons, m, op) and returns: pt, op, reduce