code: def forward(self, x):
      sl = x.shape[1]
      if self.relative:
          start = random.randint(sl, self.seq_len) - sl
          return self.emb(
              torch.arange(start, start + sl, device=x.device)
          )
      else:
          return self.emb(torch.arange(0, sl, device=x.device))
  
imports: import functools
  import math
  import random
  import torch
  import torch.nn
  import torch.nn.functional
  from transformers import GPT2Config
  from TTS.tts.layers.xtts.gpt_inference import GPT2InferenceModel
  from TTS.tts.layers.xtts.latent_encoder import ConditioningEncoder
  from TTS.tts.layers.xtts.perceiver_encoder import PerceiverResampler
label_desc: Use image node_id 2 for calling the LearnedPositionEmbeddings obj's underlying member method code with example usage: obj.forward(x) and returns: self, self