code: def __init__(self, seq_len, model_dim, init=0.02):
      super().__init__()
      self.emb = nn.Embedding(seq_len, model_dim)
      # Initializing this way is standard for GPT-2
      self.emb.weight.data.normal_(mean=0.0, std=init)
  
imports: import functools
  import torch
  import torch.nn
  import torch.nn.functional
  from transformers import GPT2Config, GPT2PreTrainedModel, LogitsProcessorList
  from transformers.modeling_outputs import CausalLMOutputWithCrossAttentions
  from TTS.tts.layers.tortoise.arch_utils import AttentionBlock, TypicalLogitsWarper
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)