class_docstr: wandb sweep.
  
  Args:
      config (DictConfig): Original model configuration.
      sweep_config (DictConfig): Sweep configuration.
      entity (str, optional): Username or workspace to send the project to. Defaults to None.
class_docstr_tok: ["wandb","sweep",".","Args",":","config","(","DictConfig",")",":","Original","model","configuration",".","sweep_config","(","DictConfig",")",":","Sweep","configuration",".","entity","(","str",",","optional",")",":","Username","or","workspace","to","send","the","project","to",".","Defaults","to","None","."]
docstr: Run the sweep.
docstr_tok: ["Run","the","sweep","."]
code: def run(self) -> None:
      """Run the sweep."""
      flattened_hpo_params = flatten_hpo_params(
          self.sweep_config.parameters
      )
      self.sweep_config.parameters = flattened_hpo_params
      sweep_id = wandb.sweep(
          OmegaConf.to_object(self.sweep_config),
          project=f"{self.config.model.name}_{self.config.dataset.name}",
          entity=self.entity,
      )
      wandb.agent(
          sweep_id, function=self.sweep, count=self.observation_budget
      )
  
imports: from __future__ import annotations
  import gc
  import pytorch_lightning
  import torch
  import wandb
  from comet_ml import Optimizer
  from omegaconf import DictConfig, ListConfig, OmegaConf
  from pytorch_lightning.loggers import CometLogger, WandbLogger
  from anomalib.config import update_input_size_config
  from anomalib.data import get_datamodule
  from anomalib.models import get_model
  from anomalib.utils.sweep import flatten_sweep_params, get_sweep_callbacks, set_in_nested_config
  from .config import flatten_hpo_params
label_desc: Use image node_id 2 for calling the WandbSweep obj's underlying member method code with example usage: obj.run() without return types