class_docstr: This class implements the normalized embedding layer for the transformer.
  Since the dot product of the self-attention is always normalized by sqrt(d_model)
  and the final linear projection for prediction shares weight with the embedding layer,
  we multiply the output of the embedding by sqrt(d_model).
  Arguments
  ---------
  d_model: int
      The number of expected features in the encoder/decoder inputs (default=512).
  vocab: int
      The vocab size.
  Example
  -------
  >>> emb = NormalizedEmbedding(512, 1000)
  >>> trg = torch.randint(0, 999, (8, 50))
  >>> emb_fea = emb(trg)
docstr: Processes the input tensor x and returns an output tensor.
docstr_tok: ["Processes","the","input","tensor","x","and","returns","an","output","tensor","."]
code: def forward(self, x):
      """Processes the input tensor x and returns an output tensor."""
      return self.emb(x) * math.sqrt(self.d_model)
  
imports: import math
  import torch
  import torch.nn
  import speechbrain
  from typing import Optional
  import numpy
  from .Conformer import ConformerEncoder
  from .Branchformer import BranchformerEncoder
  from speechbrain.nnet.activations import Swish
  from speechbrain.nnet.attention import RelPosEncXL
  from speechbrain.nnet.CNN import Conv1d
label_desc: Use image node_id 2 for calling the NormalizedEmbedding obj's underlying member method code with example usage: obj.forward(x) and returns: unknown