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 __init__(
      self,
      device="auto",
      num_epochs=400,
      disable_progress=False,
      hardware_metric_limit=None,
  ):
      super().__init__(device)
      self.num_epochs = num_epochs
      self.disable_progress = disable_progress
      self.hardware_metric_limit = hardware_metric_limit
  
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 1 to create a new RandomSearch object from inherited base classes: BaseNAS with example: obj = RandomSearch(device, num_epochs, disable_progress, hardware_metric_limit)