class_docstr: A runner class to train and evaluate a model given a task and datasets.
  
  The runner uses pytorch distributed data parallel by default. Future release
  will support other distributed frameworks.
class_docstr_tok: ["A","runner","class","to","train","and","evaluate","a","model","given","a","task","and","datasets",".","The","runner","uses","pytorch","distributed","data","parallel","by","default",".","Future","release","will","support","other","distributed","frameworks","."]
code: def optimizer(self):
      # TODO make optimizer class and configurations
      if self._optimizer is None:
          lr_scale = self.config.run_cfg.get("lr_layer_decay", 1)
          weight_decay = self.config.run_cfg.get("weight_decay", 0.05)
          optim_params = self._model.get_optimizer_params(
              weight_decay, lr_scale
          )
  
          num_parameters = 0
          for p_group in optim_params:
              for p in p_group["params"]:
                  num_parameters += p.data.nelement()
          logging.info(
              "number of trainable parameters: {}".format(
                  num_parameters
              )
          )
  
          beta2 = self.config.run_cfg.get("beta2", 0.999)
  
          self._optimizer = torch.optim.AdamW(
              optim_params,
              lr=float(self.config.run_cfg.init_lr),
              betas=(0.9, beta2),
          )
      return self._optimizer
  
imports: import datetime
  import json
  import logging
  import os
  import time
  from pathlib import Path
  import torch
  import torch.distributed
  import webdataset
  from lavis.common.dist_utils import download_cached_file, get_rank, get_world_size, is_main_process, main_process
  from lavis.common.registry import registry
  from lavis.common.utils import is_url
  from lavis.datasets.data_utils import concat_datasets, reorg_datasets_by_split
  from lavis.datasets.datasets.dataloader_utils import IterLoader, MultiIterLoader, PrefetchLoader
  from torch.nn.parallel import DistributedDataParallel
  from torch.utils.data import DataLoader, DistributedSampler
  from torch.utils.data.dataset import ChainDataset
label_desc: Use image node_id 5 for calling the RunnerBase obj's underlying member method code with example usage: obj.optimizer() and returns: self