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 torch
      >>> from doctr.models import vitstr_base
      >>> model = vitstr_base(pretrained=False)
      >>> input_tensor = torch.rand((1, 3, 32, 128))
      >>> 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,
          "1",
          embedding_units=768,
          patch_size=(4, 8),
          ignore_keys=["head.weight", "head.bias"],
          **kwargs,
      )
  
imports: from copy import deepcopy
  from typing import Any, Callable, Dict, List, Optional, Tuple
  import torch
  from torch import nn
  from torch.nn import functional
  from torchvision.models._utils import IntermediateLayerGetter
  from doctr.datasets import VOCABS
  from ...classification import vit_b, vit_s
  from ...utils.pytorch 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