code: def __call__(
      self,
      sequences: Union[str, List[str]],
      candidate_labels: List[str],
      *args,
      **kwargs,
  ):
      metadata = (kwargs.pop("metadata", None) or {}).copy()
      hypothesis_template = kwargs.get(
          "hypothesis_template", "@default"
      )
      multi_label = kwargs.get(
          "multi_label", kwargs.get("multi_class", False)
      )
      batch_predictions = self.__model__(
          sequences, candidate_labels, *args, **kwargs
      )
      try:
          if not isinstance(sequences, list):
              sequences = [sequences]
  
          predictions = batch_predictions
          if not isinstance(batch_predictions, list):
              predictions = [batch_predictions]
  
          predictions = [
              [
                  LabelPrediction(label=label, score=score)
                  for label, score in zip(
                      prediction["labels"], prediction["scores"]
                  )
              ]
              for prediction in predictions
          ]
  
          if not metadata:
              metadata = [{}] * len(sequences)
          elif not isinstance(metadata, list):
              metadata = [metadata]
          for meta in metadata:
              meta.update(
                  {
                      "labels": candidate_labels,
                      "hypothesis_template": hypothesis_template,
                  }
              )
          filtered_data = [
              (input_, meta, predictions_)
              for input_, meta, predictions_ in zip(
                  sequences, metadata, predictions
              )
              if self.is_record_accepted()
          ]
          if filtered_data:
              self.send_records(filtered_data, multi_label=multi_label)
  
      finally:
          return batch_predictions
  
imports: from datetime import datetime
  from typing import Any, Dict, List, Optional, Tuple, Union
  from argilla.client.api import Api
  from argilla.client.models import TextClassificationRecord
  from argilla.monitoring.base import BaseMonitor
  from argilla.monitoring.types import MissingType
  from argilla.pydantic_v1 import BaseModel
label_desc: Use image node_id 1 for calling the ZeroShotMonitor obj's underlying member method code with example usage: obj.__call__(sequences, candidate_labels) and returns: batch_predictions