code: def _execute_reduce(cls, ctx, op: "FancyIndexingDistribute"):
      fancy_indexes = []
      poses = []
      xp = None
      for fancy_index, pos in op.iter_mapper_data(ctx):
          if xp is None:
              xp = get_array_module(fancy_index)
          if fancy_index.size == 0:
              fancy_index = fancy_index.reshape(len(op.axes), 0)
          fancy_indexes.append(fancy_index)
          poses.append(pos)
  
      fancy_index = np.hstack(fancy_indexes)
      pos = np.hstack(poses)
  
      assert len(op.outputs) - 1 == len(fancy_index)
      for out_chunk, axis_fancy_index in zip(
          op.outputs[:-1], fancy_index
      ):
          ctx[out_chunk.key] = axis_fancy_index
      ctx[op.outputs[-1].key] = np.asarray([len(p) for p in poses]), pos
  
imports: from numbers import Integral
  import numpy
  from ...None import opcodes
  from ...core import ENTITY_TYPE
  from ...core.operand import OperandStage
  from ...serialization.serializables import FieldTypes, KeyField, ListField, TupleField, Int32Field
  from ..core import TENSOR_TYPE, TensorOrder
  from ..utils import split_indexes_into_chunks, calc_pos, filter_inputs
  from ..operands import TensorHasInput, TensorOperandMixin, TensorMapReduceOperand
  from ..array_utils import get_array_module
  from .core import process_index, calc_shape
  from .index_lib import TensorIndexesHandler
label_desc: Use image node_id 7 for calling the FancyIndexingDistribute obj's underlying member method code with example usage: obj._execute_reduce(cls, ctx, op) without return types