class_docstr: Implements the character embedding module
  
  Args:
  ----
      vocab_size: size of the vocabulary
      d_model: dimension of the model
class_docstr_tok: ["Implements","the","character","embedding","module","Args",":","--","--","vocab_size",":","size","of","the","vocabulary","d_model",":","dimension","of","the","model"]
code: def __init__(self, vocab_size: int, d_model: int):
      super().__init__()
      self.embedding = nn.Embedding(vocab_size, d_model)
      self.d_model = d_model
  
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 to create a new CharEmbedding object from inherited base classes: nn with example: obj = CharEmbedding(vocab_size, d_model)