docstr: Parameters
  ----------
  x : torch.Tensor
  input tensor with shape batch_size, seq_len, embed_dim
  Returns
  -------
  pos_emb : torch.Tensor
docstr_tok: ["Parameters","--","--","--","--","--","x",":","torch.Tensor","input","tensor","with","shape","batch_size",",","seq_len",",","embed_dim","Returns","--","--","--","-","pos_emb",":","torch.Tensor"]
code: def forward(self, x: torch.Tensor):
      """
      Parameters
      ----------
      x : torch.Tensor
      input tensor with shape batch_size, seq_len, embed_dim
      Returns
      -------
      pos_emb : torch.Tensor
      """
      seq_len = x.size(1)
      with torch.no_grad():
          tot_pe = torch.zeros(
              (2, seq_len, self.emb_dim), dtype=x.dtype
          ).to(x)
          pe_past = tot_pe[0]
          pe_future = tot_pe[1]
          positions = (
              torch.arange(0, seq_len, dtype=x.dtype, device=x.device)
              .to(x)
              .unsqueeze(-1)
          )
          sinusoids = torch.sin(positions * self.inv_freq)
          pe_past[:, 0::2] = sinusoids
          pe_past[:, 1::2] = torch.cos(positions * self.inv_freq)
          pe_future[:, 0::2] = sinusoids  # same for past and future
          pe_future[:, 1::2] = torch.cos(-positions * self.inv_freq)
  
          pe_past = torch.flip(pe_past, (0,)).unsqueeze(0)
          pe_future = pe_future[1:].unsqueeze(0)
          pe = torch.cat([pe_past, pe_future], dim=1)
          # pe is now 1, 2*seq_len, embed_dim
          return pe
  
imports: import torch
  import logging
  import torch.nn
  import numpy
  from typing import Optional
  from speechbrain.dataio.dataio import length_to_mask
  import torch.nn.functional
  import math
label_desc: Use image node_id 2 for calling the RelPosEncXL obj's underlying member method code with example usage: obj.forward(x) and returns: pe