class_docstr: Uniformly random architecture search
  
  Parameters
  ----------
  device : str or torch.device
      The device of the whole process, e.g. "cuda", torch.device("cpu")
  num_epochs : int
      Number of epochs planned for training.
  disable_progeress: boolean
      Control whether show the progress bar.
class_docstr_tok: ["Uniformly","random","architecture","search","Parameters","--","--","--","--","--","device",":","str","or","torch.device","The","device","of","the","whole","process",",","e.g",".","``","cuda","''",",","torch.device","(","``","cpu","''",")","num_epochs",":","int","Number","of","epochs","planned","for","training",".","disable_progeress",":","boolean","Control","whether","show","the","progress","bar","."]
code: def sample(self):
      # uniformly sample
      selection = {}
      for k, v in self.selection_dict.items():
          selection[k] = np.random.choice(range(v))
      return selection
  
file_path: AutoGL/autogl/module/nas/algorithm/random_search.py
imports: 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
  from tqdm import tqdm
  from .rl import PathSamplingLayerChoice, PathSamplingInputChoice
  import numpy
  from ....utils import get_logger
label_desc: Use image node_id 3 for calling the RandomSearch obj's underlying member method code with example usage: obj.sample() and returns: selection