code: def get_grad_norm_parameter_groups(self):
      groups = {
          "minicoder": list(self.contextual_embedder.parameters()),
          "layers": list(self.layers.parameters()),
          "code_converters": list(self.code_embedding.parameters())
          + list(self.code_converter.parameters())
          + list(self.latent_conditioner.parameters())
          + list(self.latent_conditioner.parameters()),
          "timestep_integrator": list(
              self.conditioning_timestep_integrator.parameters()
          )
          + list(self.integrating_conv.parameters()),
          "time_embed": list(self.time_embed.parameters()),
      }
      return groups
  
imports: import math
  import random
  from abc import abstractmethod
  import torch
  import torch.nn
  import torch.nn.functional
  from torch import autocast
  from TTS.tts.layers.tortoise.arch_utils import AttentionBlock, normalization
label_desc: Use image node_id 2 for calling the DiffusionTts obj's underlying member method code with example usage: obj.get_grad_norm_parameter_groups() and returns: groups