class_docstr: Override GPT2LMHeadModel to allow for prefix conditioning.
code: def __init__(
      self, config, gpt, pos_emb, embeddings, norm, linear, kv_cache
  ):
      super().__init__(config)
      self.transformer = gpt
      self.pos_embedding = pos_emb
      self.embeddings = embeddings
      self.final_norm = norm
      self.lm_head = nn.Sequential(norm, linear)
      self.kv_cache = kv_cache
  
file_path: TTS/TTS/tts/layers/xtts/gpt_inference.py
imports: import math
  import torch
  from torch import nn
  from transformers import GPT2PreTrainedModel
  from transformers.modeling_outputs import CausalLMOutputWithCrossAttentions
label_desc: Use image node_id 1 to create a new GPT2InferenceModel object from inherited base classes: GPT2PreTrainedModel with example: obj = GPT2InferenceModel(config, gpt, pos_emb, embeddings, norm, linear, kv_cache)