docstr: :param list[Sample] data_samples: list of Samples
  :return: dict obj to save metrics result
code: def evaluate(self, data_samples, prefix=""):
      r"""
      :param list[Sample] data_samples: list of Samples
      :return: dict obj to save metrics result
      """
  
      dataset = self.encode(data_samples)
      data_iterator = torch.utils.data.DataLoader(
          dataset=dataset,
          batch_size=self.batch_size,
          collate_fn=self.collate_fn,
      )
  
      if not self.metrics:
          self.metrics = TASK_METRICS[self.task][0]["fun"](
              encoding_type=self.encoding_type, tag_vocab=self.tag
          )
  
      with torch.no_grad():
          for batch_x, batch_y in data_iterator:
              pred = self.model(batch_x)  # [batch, seq_len]
              target = batch_y  # [batch, seq_len]
              seq_len = [torch.tensor(len(i)) for i in batch_x]
              self.metrics.evaluate(pred, target, torch.stack(seq_len))
  
      metric = self.metrics.get_metric()
      score = {}
      for i in metric.keys():
          score[prefix + self.metrics2score[i]] = metric[i]
      print(score)
      return score
  
file_path: textflint/textflint/input/model/flint_model/flint_model_cws.py
imports: import torch
  from .flint_model import FlintModel, TASK_METRICS
label_desc: Use image node_id 2 for calling the FlintModelCWS obj's underlying member method code with example usage: obj.evaluate(data_samples, prefix) and returns: score