code: def _execute_reduce(cls, ctx, op: "FancyIndexingConcat"):
      fancy_index_axis = op.fancy_index_axis
      fancy_index_shape = op.fancy_index_shape
  
      indexed_arrays = []
      poses = []
      for index_array, pos in op.iter_mapper_data(ctx):
          indexed_arrays.append(index_array)
          poses.append(pos)
  
      concat_array = get_array_module(indexed_arrays[0]).concatenate(
          indexed_arrays, axis=fancy_index_axis
      )
      concat_pos = get_array_module(poses[0]).hstack(poses)
      select_pos = calc_pos(
          fancy_index_shape, concat_pos, xp=get_array_module(poses[0])
      )
      select = (slice(None),) * fancy_index_axis + (select_pos,)
      ctx[op.outputs[0].key] = concat_array[select]
  
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 6 for calling the FancyIndexingConcat obj's underlying member method code with example usage: obj._execute_reduce(cls, ctx, op) without return types