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","."]
code: def __init__(
      self,
      config: DictConfig | ListConfig,
      sweep_config: DictConfig | ListConfig,
      entity: str | None = None,
  ) -> None:
      self.config = config
      self.sweep_config = sweep_config
      self.observation_budget = sweep_config.observation_budget
      self.entity = entity
      if "observation_budget" in self.sweep_config.keys():
          # this instance check is to silence mypy.
          if isinstance(self.sweep_config, DictConfig):
              self.sweep_config.pop("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 1 to create a new WandbSweep object  with example: obj = WandbSweep(config, sweep_config, entity)