code: def vitstr_base(pretrained: bool = False, **kwargs: Any) -> ViTSTR:
      """ViTSTR-Base as described in `"Vision Transformer for Fast and Efficient Scene Text Recognition"
      <https://arxiv.org/pdf/2105.08582.pdf>`_.
  
      >>> import tensorflow as tf
      >>> from doctr.models import vitstr_base
      >>> model = vitstr_base(pretrained=False)
      >>> input_tensor = tf.random.uniform(shape=[1, 32, 128, 3], maxval=1, dtype=tf.float32)
      >>> out = model(input_tensor)
  
      Args:
      ----
          pretrained (bool): If True, returns a model pre-trained on our text recognition dataset
          **kwargs: keyword arguments of the ViTSTR architecture
  
      Returns:
      -------
          text recognition architecture
      """
      return _vitstr(
          "vitstr_base",
          pretrained,
          vit_b,
          embedding_units=768,
          patch_size=(4, 8),
          **kwargs,
      )
  
file_path: doctr/doctr/models/recognition/vitstr/tensorflow.py
imports: from copy import deepcopy
  from typing import Any, Dict, List, Optional, Tuple
  import tensorflow
  from tensorflow.keras import Model, layers
  from doctr.datasets import VOCABS
  from ...classification import vit_b, vit_s
  from ...utils.tensorflow import _bf16_to_float32, load_pretrained_params
  from .base import _ViTSTR, _ViTSTRPostProcessor
label_desc: Use image node_id 3 for calling a global function with example usage: vitstr_base(pretrained) and returns: _vitstr