docstr: `predict` takes a string or list of strings and returns a list of dictionaries, each dictionary
  containing the text, the predicted label, and the confidence score.
  
  Args:
    text (Union[List[str], str]): The text to be classified.
    as_argilla_records (bool): If True, the output will be a list of Argilla records instead of dictionaries. Defaults to True.
  
  Returns:
    A list of predictions or Argilla records.
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code: def predict(
      self,
      text: Union[List[str], str],
      as_argilla_records: bool = True,
      **kwargs,
  ):
      """
      `predict` takes a string or list of strings and returns a list of dictionaries, each dictionary
      containing the text, the predicted label, and the confidence score.
  
      Args:
        text (Union[List[str], str]): The text to be classified.
        as_argilla_records (bool): If True, the output will be a list of Argilla records instead of dictionaries. Defaults to True.
  
      Returns:
        A list of predictions or Argilla records.
      """
      return self._trainer.predict(
          text=text, as_argilla_records=as_argilla_records, **kwargs
      )
  
imports: import logging
  import os
  import warnings
  from abc import ABC, abstractmethod
  from typing import TYPE_CHECKING, Any, Dict, List, Optional, Union
  from argilla.client.datasets import DatasetForText2Text, DatasetForTextClassification, DatasetForTokenClassification
  from argilla.client.models import Framework, Text2TextRecord, TextClassificationRecord, TokenClassificationRecord
  from argilla.client.singleton import active_client
  from argilla.datasets import TextClassificationSettings, TokenClassificationSettings, load_dataset_settings
  from argilla.utils.telemetry import get_telemetry_client
label_desc: Use image node_id 5 for calling the ArgillaTrainer obj's underlying member method code with example usage: obj.predict(text, as_argilla_records) and returns: self