code: def vit_s(
      pretrained: bool = False, **kwargs: Any
  ) -> VisionTransformer:
      """VisionTransformer-S architecture
      `"An Image is Worth 16x16 Words: Transformers for Image Recognition at Scale",
      <https://arxiv.org/pdf/2010.11929.pdf>`_. Patches: (H, W) -> (H/8, W/8)
  
      NOTE: unofficial config used in ViTSTR and ParSeq
  
      >>> import tensorflow as tf
      >>> from doctr.models import vit_s
      >>> model = vit_s(pretrained=False)
      >>> input_tensor = tf.random.uniform(shape=[1, 32, 32, 3], maxval=1, dtype=tf.float32)
      >>> out = model(input_tensor)
  
      Args:
      ----
          pretrained: boolean, True if model is pretrained
          **kwargs: keyword arguments of the VisionTransformer architecture
  
      Returns:
      -------
          A feature extractor model
      """
      return _vit(
          "vit_s",
          pretrained,
          d_model=384,
          num_layers=12,
          num_heads=6,
          ffd_ratio=4,
          **kwargs,
      )
  
file_path: doctr/doctr/models/classification/vit/tensorflow.py
imports: from copy import deepcopy
  from typing import Any, Dict, Optional, Tuple
  import tensorflow
  from tensorflow.keras import Sequential, layers
  from doctr.datasets import VOCABS
  from doctr.models.modules.transformer import EncoderBlock
  from doctr.models.modules.vision_transformer.tensorflow import PatchEmbedding
  from doctr.utils.repr import NestedObject
  from ...utils import load_pretrained_params
label_desc: Use image node_id 2 for calling a global function with example usage: vit_s(pretrained) and returns: _vit