code: def reduced_binary_einsum(arr0, sub0, arr1, sub1, sub_others):
      set0 = set(sub0)
      set1 = set(sub1)
      assert len(set0) == len(
          sub0
      ), "operand 0 should be reduced: diagonal"
      assert len(set1) == len(
          sub1
      ), "operand 1 should be reduced: diagonal"
  
      if len(sub0) == 0 or len(sub1) == 0:
          return arr0 * arr1, sub0 + sub1
  
      set_others = set(sub_others)
      shared = set0 & set1
      batch_dims = shared & set_others
      contract_dims = shared - batch_dims
  
      bs0, cs0, ts0 = _make_transpose_axes(
          sub0, batch_dims, contract_dims
      )
      bs1, cs1, ts1 = _make_transpose_axes(
          sub1, batch_dims, contract_dims
      )
  
      sub_b = [sub0[axis] for axis in bs0]
      assert sub_b == [sub1[axis] for axis in bs1]
      sub_l = [sub0[axis] for axis in ts0]
      sub_r = [sub1[axis] for axis in ts1]
  
      sub_out = sub_b + sub_l + sub_r
      assert (
          set(sub_out) <= set_others
      ), "operands should be reduced: unary sum"
  
      if len(contract_dims) == 0:
          # Use element-wise multiply when no contraction is needed
          if len(sub_out) == len(sub_others):
              # to assure final output of einsum is C-contiguous
              sub_out = sub_others
          arr0 = _expand_dims_transpose(arr0, sub0, sub_out)
          arr1 = _expand_dims_transpose(arr1, sub1, sub_out)
          return arr0 * arr1, sub_out
  
      for accelerator in _accelerator.get_routine_accelerators():
          if (
              accelerator == _accelerator.ACCELERATOR_CUTENSOR
              and cutensor is not None
          ):
              if _use_cutensor(
                  arr0.dtype,
                  sub0,
                  arr1.dtype,
                  sub1,
                  batch_dims,
                  contract_dims,
              ):
                  if len(sub_out) == len(sub_others):
                      # to assure final output of einsum is C-contiguous
                      sub_out = sub_others
                  out_shape = _get_out_shape(
                      arr0.shape, sub0, arr1.shape, sub1, sub_out
                  )
                  arr_out = cupy.empty(out_shape, arr0.dtype)
                  arr0 = cupy.ascontiguousarray(arr0)
                  arr1 = cupy.ascontiguousarray(arr1)
                  arr_out = cutensor.contraction(
                      1.0, arr0, sub0, arr1, sub1, 0.0, arr_out, sub_out
                  )
                  return arr_out, sub_out
  
      tmp0, shapes0 = _flatten_transpose(arr0, [bs0, ts0, cs0])
      tmp1, shapes1 = _flatten_transpose(arr1, [bs1, cs1, ts1])
      shapes_out = shapes0[0] + shapes0[1] + shapes1[2]
      assert shapes0[0] == shapes1[0]
      arr_out = cupy.matmul(tmp0, tmp1).reshape(shapes_out)
      return arr_out, sub_out
  
imports: import copy
  import itertools
  import operator
  import string
  import warnings
  import cupy
  from cupy._core import _accelerator
  from cupy import _util
  from cupy.linalg._einsum_opt import _greedy_path
  from cupy.linalg._einsum_opt import _optimal_path
  from cupy.linalg._einsum_cutn import _try_use_cutensornet
label_desc: Use image node_id 12 for calling a global function with example usage: reduced_binary_einsum(arr0, sub0, arr1, sub1, sub_others) and returns: arr_out,  sub_out, unknown,  unknown, unknown,  sub_out, arr_out,  sub_out