class_docstr: This class implements the absolute sinusoidal positional encoding function.
  PE(pos, 2i)   = sin(pos/(10000^(2i/dmodel)))
  PE(pos, 2i+1) = cos(pos/(10000^(2i/dmodel)))
  Arguments
  ---------
  input_size: int
      Embedding dimension.
  max_len : int, optional
      Max length of the input sequences (default 2500).
  Example
  -------
  >>> a = torch.rand((8, 120, 512))
  >>> enc = PositionalEncoding(input_size=a.shape[-1])
  >>> b = enc(a)
  >>> b.shape
  torch.Size([1, 120, 512])
docstr: Arguments
  ---------
  x : tensor
      Input feature shape (batch, time, fea)
docstr_tok: ["Arguments","--","--","--","--","-","x",":","tensor","Input","feature","shape","(","batch",",","time",",","fea",")"]
code: def forward(self, x):
      """
      Arguments
      ---------
      x : tensor
          Input feature shape (batch, time, fea)
      """
      return self.pe[:, : x.size(1)].clone().detach()
  
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 PositionalEncoding obj's underlying member method code with example usage: obj.forward(x) and returns: self