code: def execute_reduce(cls, ctx, op: "LearnShuffle"):
      inputs_grid = np.empty(op.reduce_sizes, dtype=object)
      for input_index, inp in op.iter_mapper_data(ctx):
          reduce_index = tuple(input_index[ax] for ax in op.axes)
          inputs_grid[reduce_index] = inp
      ret = cls._concat_grid(inputs_grid, op.axes, op.output_types[0])
      for ax, seed in zip(op.axes, op.seeds):
          ind = np.random.RandomState(seed).permutation(
              np.arange(ret.shape[ax])
          )
          slc = (slice(None),) * ax + (ind,)
          ret = _safe_slice(ret, slc, op.output_types[0])
      ctx[op.outputs[0].key] = ret
  
imports: import itertools
  from collections import defaultdict
  from collections.abc import Iterable
  from functools import reduce
  import numpy
  import pandas
  from ...None import opcodes
  from ...core import get_output_types, recursive_tile
  from ...core.operand import OperandStage, MapReduceOperand
  from ...dataframe.utils import parse_index
  from ...lib import sparse
  from ...serialization.serializables import FieldTypes, TupleField, KeyField
  from ...tensor.utils import validate_axis, check_random_state, gen_random_seeds, decide_unify_split
  from ...tensor.array_utils import get_array_module
  from ...utils import tokenize, lazy_import, has_unknown_shape
  from ...core import ExecutableTuple
  from ..operands import LearnOperandMixin, OutputType, LearnShuffleProxy
  from ..utils import convert_to_tensor_or_dataframe
label_desc: Use image node_id 17 for calling the LearnShuffle obj's underlying member method code with example usage: obj.execute_reduce(cls, ctx, op) without return types