code: def __init__(self, seq_len, model_dim, init=0.02, relative=False):
      super().__init__()
      # nn.Embedding
      self.emb = torch.nn.Embedding(seq_len, model_dim)
      # Initializing this way is standard for GPT-2
      self.emb.weight.data.normal_(mean=0.0, std=init)
      self.relative = relative
      self.seq_len = seq_len
  
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 1 to create a new LearnedPositionEmbeddings object from inherited base classes: nn with example: obj = LearnedPositionEmbeddings(seq_len, model_dim, init, relative)