code: def get_grad_norm_parameter_groups(self):
      return {
          "conditioning_encoder": list(
              self.conditioning_encoder.parameters()
          ),
          "conditioning_perceiver": list(
              self.conditioning_perceiver.parameters()
          )
          if self.use_perceiver_resampler
          else None,
          "gpt": list(self.gpt.parameters()),
          "heads": list(self.text_head.parameters())
          + list(self.mel_head.parameters()),
      }
  
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 GPT obj's underlying member method code with example usage: obj.get_grad_norm_parameter_groups() and returns: dict