docstr: Predict the labels for the given text using the trained pipeline.
  
  Args:
      text: A `str` or a `List[str]` with the text to predict the labels for.
      as_argilla_records:
          A `bool` indicating whether to return the predictions as `argilla` records
          or as `dicts`. Defaults to True.
  
  Returns:
      Either a `dict`, `BaseModel` (if `as_argilla_records` is True) or a `List[dict]`,
      `List[BaseModel]` (if `as_argilla_records` is True) with the predictions.
docstr_tok: ["Predict","the","labels","for","the","given","text","using","the","trained","pipeline",".","Args",":","text",":","A","`","str","`","or","a","`","List","[","str","]","`","with","the","text","to","predict","the","labels","for",".","as_argilla_records",":","A","`","bool","`","indicating","whether","to","return","the","predictions","as","`","argilla","`","records","or","as","`","dicts","`",".","Defaults","to","True",".","Returns",":","Either","a","`","dict","`",",","`","BaseModel","`","(","if","`","as_argilla_records","`","is","True",")","or","a","`","List","[","dict","]","`",",","`","List","[","BaseModel","]","`","(","if","`","as_argilla_records","`","is","True",")","with","the","predictions","."]
code: def predict(
      self,
      text: Union[List[str], str],
      as_argilla_records: bool = True,
      **kwargs,
  ) -> Union[
      Dict[str, Any], List[Dict[str, Any]], BaseModel, List[BaseModel]
  ]:
      """Predict the labels for the given text using the trained pipeline.
  
      Args:
          text: A `str` or a `List[str]` with the text to predict the labels for.
          as_argilla_records:
              A `bool` indicating whether to return the predictions as `argilla` records
              or as `dicts`. Defaults to True.
  
      Returns:
          Either a `dict`, `BaseModel` (if `as_argilla_records` is True) or a `List[dict]`,
          `List[BaseModel]` (if `as_argilla_records` is True) with the predictions.
      """
      if self.trainer_model is None:
          self._logger.warning("Using model without fine-tuning.")
          self.init_model()
  
      str_input = False
      if isinstance(text, str):
          text = [text]
          str_input = True
  
      formatted_prediction = []
      docs = self.trainer_model.pipe(text, **kwargs)
      if as_argilla_records:
          for doc in docs:
              if "ner" in self._spacy_pipeline_components:
                  entities = [
                      (ent.label_, ent.start_char, ent.end_char)
                      for ent in doc.ents
                  ]
                  pred = {
                      "text": doc.text,
                      "tokens": [t.text for t in doc],
                      "prediction": entities,
                  }
                  pred = self._record_class(**pred)
              elif any(
                  [
                      p in self._spacy_pipeline_components
                      for p in ["textcat", "textcat_multilabel"]
                  ]
              ):
                  pred = {
                      "text": doc.text,
                      "prediction": [
                          (k, v) for k, v in doc.cats.items()
                      ],
                  }
                  pred = self._record_class(
                      **pred, multi_label=self._multi_label
                  )
              else:
                  continue
              formatted_prediction.append(pred)
      else:
          formatted_prediction = docs
  
      if str_input:
          formatted_prediction = list(formatted_prediction)[0]
      return formatted_prediction
  
imports: import logging
  import sys
  from pathlib import Path
  from typing import Any, Dict, List, Literal, Optional, Union
  from pydantic import BaseModel
  from argilla.client.models import TextClassificationRecord, TokenClassificationRecord
  from argilla.training.base import ArgillaTrainerSkeleton
  from argilla.utils.dependency import require_dependencies
label_desc: Use image node_id 7 for calling the _ArgillaSpaCyTrainerBase obj's underlying member method code with example usage: obj.predict(text, as_argilla_records) and returns: formatted_prediction