class_docstr: Implementation of Repeat in a path-sampling supernet.
  Samples one / some of the prefixes of the repeated blocks.
  
  Attributes
  ----------
  _sampled : int or list of int
      Sampled depth.
class_docstr_tok: ["Implementation","of","Repeat","in","a","path-sampling","supernet",".","Samples","one","\/","some","of","the","prefixes","of","the","repeated","blocks",".","Attributes","--","--","--","--","--","_sampled",":","int","or","list","of","int","Sampled","depth","."]
code: def __init__(
      self, blocks: list[nn.Module], depth: MutableExpression[int]
  ):
      super().__init__(blocks, depth)
      self._sampled: list[int] | int | None = None
  
imports: from __future__ import annotations
  import copy
  import functools
  import random
  from typing import Any, List, Dict, Sequence, cast
  import torch
  import torch.nn
  from nni.mutable import MutableExpression, label_scope, Mutable, Categorical, CategoricalMultiple
  from nni.nas.nn.pytorch import LayerChoice, InputChoice, Repeat, Cell
  from nni.nas.nn.pytorch.cell import CellOpFactory, create_cell_op_candidates, preprocess_cell_inputs
  from .base import BaseSuperNetModule
  from ._expression_utils import weighted_sum
  from .operation import MixedOperationSamplingPolicy, MixedOperation
label_desc: Use image node_id 1 to create a new PathSamplingRepeat object from inherited base classes: Repeat, BaseSuperNetModule with example: obj = PathSamplingRepeat(blocks, depth)