code: def execute_single(cls, ctx, op):
      x = ctx[op.inputs[0].key]
      conv = lambda x: x
      if op.output_types[0] == OutputType.tensor:
          xp = get_array_module(x)
          if xp is sparse:
              conv = lambda x: x
          else:
              conv = (
                  xp.ascontiguousarray
                  if op.outputs[0].order.value == "C"
                  else xp.asfortranarray
              )
  
      for axis, seed in zip(op.axes, op.seeds):
          size = x.shape[axis]
          ind = np.random.RandomState(seed).permutation(np.arange(size))
          slc = (slice(None),) * axis + (ind,)
          x = _safe_slice(x, slc, op.output_types[0])
  
      ctx[op.outputs[0].key] = conv(x)
  
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 15 for calling the LearnShuffle obj's underlying member method code with example usage: obj.execute_single(cls, ctx, op) without return types