class_docstr: Post processor for PARSeq architecture
  
  Args:
  ----
      vocab: string containing the ordered sequence of supported characters
class_docstr_tok: ["Post","processor","for","PARSeq","architecture","Args",":","--","--","vocab",":","string","containing","the","ordered","sequence","of","supported","characters"]
code: def __call__(
      self,
      logits: torch.Tensor,
  ) -> List[Tuple[str, float]]:
      # compute pred with argmax for attention models
      out_idxs = logits.argmax(-1)
      preds_prob = torch.softmax(logits, -1).max(dim=-1)[0]
  
      # Manual decoding
      word_values = [
          "".join(self._embedding[idx] for idx in encoded_seq).split(
              "<eos>"
          )[0]
          for encoded_seq in out_idxs.cpu().numpy()
      ]
      # compute probabilties for each word up to the EOS token
      probs = [
          preds_prob[i, : len(word)].clip(0, 1).mean().item()
          if word
          else 0.0
          for i, word in enumerate(word_values)
      ]
  
      return list(zip(word_values, probs))
  
imports: import math
  from copy import deepcopy
  from itertools import permutations
  from typing import Any, Callable, Dict, List, Optional, Tuple, Union
  import numpy
  import torch
  from torch import nn
  from torch.nn import functional
  from torchvision.models._utils import IntermediateLayerGetter
  from doctr.datasets import VOCABS
  from doctr.models.modules.transformer import MultiHeadAttention, PositionwiseFeedForward
  from ...classification import vit_s
  from ...utils.pytorch import _bf16_to_float32, load_pretrained_params
  from .base import _PARSeq, _PARSeqPostProcessor
label_desc: Use image node_id 1 for calling the PARSeqPostProcessor obj's underlying member method code with example usage: obj.__call__(logits) and returns: list