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 call(self, x: tf.Tensor, **kwargs: Any) -> tf.Tensor:
      return math.sqrt(self.d_model) * self.embedding(x, **kwargs)
  
imports: import math
  from copy import deepcopy
  from itertools import permutations
  from typing import Any, Dict, List, Optional, Tuple
  import numpy
  import tensorflow
  from tensorflow.keras import Model, layers
  from doctr.datasets import VOCABS
  from doctr.models.modules.transformer import MultiHeadAttention, PositionwiseFeedForward
  from ...classification import vit_s
  from ...utils.tensorflow import _bf16_to_float32, load_pretrained_params
  from .base import _PARSeq, _PARSeqPostProcessor
label_desc: Use image node_id 2 for calling the CharEmbedding obj's underlying member method code with example usage: obj.call(x) and returns: unknown