docstr: Initialize the `ArgillaSpaCyTransformersTrainer` class.
  
  Args:
      update_transformer: A `bool` indicating whether to update the transformer
          weights during the training. Defaults to True.
      **kwargs: The `ArgillaSpaCyTrainerBase` arguments.
docstr_tok: ["Initialize","the","`","ArgillaSpaCyTransformersTrainer","`","class",".","Args",":","update_transformer",":","A","`","bool","`","indicating","whether","to","update","the","transformer","weights","during","the","training",".","Defaults","to","True",".","*","*","kwargs",":","The","`","ArgillaSpaCyTrainerBase","`","arguments","."]
code: def __init__(self, update_transformer: bool = True, **kwargs) -> None:
      """Initialize the `ArgillaSpaCyTransformersTrainer` class.
  
      Args:
          update_transformer: A `bool` indicating whether to update the transformer
              weights during the training. Defaults to True.
          **kwargs: The `ArgillaSpaCyTrainerBase` arguments.
      """
      require_dependencies(
          [
              "spacy>=3.5.3",
              "spacy-transformers",
          ]  # Required to generate the `spacy-transformers` configuration
      )
      self.update_transformer = update_transformer
      super().__init__(**kwargs)
  
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 1 to create a new ArgillaSpaCyTransformersTrainer object from inherited base classes: _ArgillaSpaCyTrainerBase with example: obj = ArgillaSpaCyTransformersTrainer(update_transformer)