docstr: Run evaluation and returns metrics.
  
  Args:
      eval_dataset(paddle.io.Dataset) : The validation dataset
      batch_size(int) : Batch size of per step, default is 1.
      num_workers(int) : Number of subprocess to load data, default is 0.
docstr_tok: ["Run","evaluation","and","returns","metrics",".","Args",":","eval_dataset","(","paddle.io.Dataset",")",":","The","validation","dataset","batch_size","(","int",")",":","Batch","size","of","per","step",",","default","is","1.","num_workers","(","int",")",":","Number","of","subprocess","to","load","data",",","default","is","0","."]
code: def evaluate(
      self,
      eval_dataset: paddle.io.Dataset,
      batch_size: int = 1,
      num_workers: int = 0,
  ) -> dict:
      """
      Run evaluation and returns metrics.
  
      Args:
          eval_dataset(paddle.io.Dataset) : The validation dataset
          batch_size(int) : Batch size of per step, default is 1.
          num_workers(int) : Number of subprocess to load data, default is 0.
      """
  
      loader = self.init_evaluate(eval_dataset, batch_size, num_workers)
      res = self.evaluate_process(loader)
      return res
  
file_path: PaddleHub/demo/autoaug/paddlehub_utils/trainer.py
imports: import os
  from collections import defaultdict
  import paddle
  from paddle.distributed import ParallelEnv
  from paddlehub.finetune.trainer import Trainer
  from paddlehub.utils.log import logger
  from paddlehub.utils.utils import Timer
label_desc: Use image node_id 8 for calling the CustomTrainer obj's underlying member method code with example usage: obj.evaluate(eval_dataset, batch_size, num_workers) and returns: res