code: def kron(A, B, format=None):
      """kronecker product of sparse matrices A and B
  
      Parameters
      ----------
      A : sparse or dense matrix
          first matrix of the product
      B : sparse or dense matrix
          second matrix of the product
      format : str, optional
          format of the result (e.g. "csr")
  
      Returns
      -------
      kronecker product in a sparse matrix format
  
  
      Examples
      --------
      >>> from scipy import sparse
      >>> A = sparse.csr_matrix(np.array([[0, 2], [5, 0]]))
      >>> B = sparse.csr_matrix(np.array([[1, 2], [3, 4]]))
      >>> sparse.kron(A, B).toarray()
      array([[ 0,  0,  2,  4],
             [ 0,  0,  6,  8],
             [ 5, 10,  0,  0],
             [15, 20,  0,  0]])
  
      >>> sparse.kron(A, [[1, 2], [3, 4]]).toarray()
      array([[ 0,  0,  2,  4],
             [ 0,  0,  6,  8],
             [ 5, 10,  0,  0],
             [15, 20,  0,  0]])
  
      """
      B = coo_matrix(B)
  
      if (format is None or format == "bsr") and 2 * B.nnz >= B.shape[
          0
      ] * B.shape[1]:
          # B is fairly dense, use BSR
          A = csr_matrix(A, copy=True)
  
          output_shape = (
              A.shape[0] * B.shape[0],
              A.shape[1] * B.shape[1],
          )
  
          if A.nnz == 0 or B.nnz == 0:
              # kronecker product is the zero matrix
              return coo_matrix(output_shape)
  
          B = B.toarray()
          data = A.data.repeat(B.size).reshape(
              -1, B.shape[0], B.shape[1]
          )
          data = data * B
  
          return bsr_matrix(
              (data, A.indices, A.indptr), shape=output_shape
          )
      else:
          # use COO
          A = coo_matrix(A)
          output_shape = (
              A.shape[0] * B.shape[0],
              A.shape[1] * B.shape[1],
          )
  
          if A.nnz == 0 or B.nnz == 0:
              # kronecker product is the zero matrix
              return coo_matrix(output_shape)
  
          # expand entries of a into blocks
          row = A.row.repeat(B.nnz)
          col = A.col.repeat(B.nnz)
          data = A.data.repeat(B.nnz)
  
          row *= B.shape[0]
          col *= B.shape[1]
  
          # increment block indices
          row, col = row.reshape(-1, B.nnz), col.reshape(-1, B.nnz)
          row += B.row
          col += B.col
          row, col = row.reshape(-1), col.reshape(-1)
  
          # compute block entries
          data = data.reshape(-1, B.nnz) * B.data
          data = data.reshape(-1)
  
          return coo_matrix(
              (data, (row, col)), shape=output_shape
          ).asformat(format)
  
imports: from __future__ import division, print_function, absolute_import
  import numpy
  from scipy._lib._numpy_compat import get_randint
  from scipy._lib.six import xrange
  from .sputils import upcast, get_index_dtype, isscalarlike
  from .csr import csr_matrix
  from .csc import csc_matrix
  from .bsr import bsr_matrix
  from .coo import coo_matrix
  from .dia import dia_matrix
  from .base import issparse
label_desc: Use image node_id 5 for calling a global function with example usage: kron(A, B, format) and returns: bsr_matrix, coo_matrix, coo_matrix, coo_matrix