class_docstr: Uniform Sampler
  
  Parameters
  ----------
  nas_modules: 
      nas_modules in NAS algorithms , including choices of modules
class_docstr_tok: ["Uniform","Sampler","Parameters","--","--","--","--","--","nas_modules",":","nas_modules","in","NAS","algorithms",",","including","choices","of","modules"]
code: def __init__(self, nas_modules):
      selection_range = {}
      for k, v in nas_modules:
          selection_range[k] = len(v)
      self.selection_dict = selection_range
  
imports: import copy
  from logging import Logger
  from numpy.core.fromnumeric import sort
  import torch
  import torch.nn
  import torch.nn.functional
  from .None import register_nas_algo
  from .base import BaseNAS
  from ..space import BaseSpace
  from ..utils import AverageMeterGroup, replace_layer_choice, replace_input_choice, get_module_order, sort_replaced_module, PathSamplingLayerChoice, PathSamplingInputChoice
  from tqdm import tqdm, trange
  from ....utils import get_logger
  import numpy
  import collections
  import dataclasses
  import random
label_desc: Use image node_id 1 to create a new UniformSampler object  with example: obj = UniformSampler(nas_modules)