code: def execute(cls, ctx, op):
      if op.stage == OperandStage.map:
          cls._execute_map(ctx, op)
      elif op.stage == OperandStage.reduce:
          cls._execute_reduce(ctx, op)
      else:
          (x,), device_id, xp = as_same_device(
              [ctx[c.key] for c in op.inputs],
              device=op.device,
              ret_extra=True,
          )
  
          with device(device_id):
              rs = xp.random.RandomState(op.seed)
              ctx[op.outputs[0].key] = _permutation_on_axis(
                  x, op.axis, rs, xp
              )
  
imports: import itertools
  from numbers import Integral
  import numpy
  from ...None import opcodes
  from ...core.operand import OperandStage
  from ...serialization.serializables import KeyField, Int32Field
  from ..operands import TensorOperandMixin, TensorShuffleProxy
  from ..utils import gen_random_seeds, validate_axis
  from ..datasource import tensor
  from ..array_utils import as_same_device, device
  from .core import TensorRandomMapReduceOperand
label_desc: Use image node_id 6 for calling the TensorPermutation obj's underlying member method code with example usage: obj.execute(cls, ctx, op) without return types