code: def _execute_reduce(cls, ctx, op):
      input_data = ctx[op.inputs[0].key].copy()
      for index_value in op.iter_mapper_data(ctx, input_id=1):
          value = index_value[0]
          indexes_with_offset = index_value[1:]
          indexes = []
          index_iter = iter(indexes_with_offset)
          for axis in range(input_data.ndim):
              if axis in op.shuffle_axes:
                  indexes.append(
                      next(index_iter) - op.chunk_offsets[axis]
                  )
          input_data[tuple(indexes)] = value
  
      ctx[op.outputs[0].key] = input_data
  
imports: import functools
  import itertools
  import operator
  from numbers import Integral
  from typing import Union
  import numpy
  from ...None import opcodes
  from ...core import ENTITY_TYPE, recursive_tile
  from ...core.context import Context
  from ...core.operand import OperandStage
  from ...serialization.serializables import KeyField, TupleField, AnyField, BoolField
  from ...tensor import tensor
  from ...utils import has_unknown_shape
  from ..base import broadcast_to
  from ..core import TENSOR_TYPE, TensorOrder
  from ..operands import TensorMapReduceOperand, TensorOperandMixin, TensorShuffleProxy
  from ..utils import broadcast_shape, filter_inputs
  from .core import process_index
label_desc: Use image node_id 12 for calling the TensorIndexSetValue obj's underlying member method code with example usage: obj._execute_reduce(cls, ctx, op) without return types