code: def __init__(
      self,
      model_channels=512,
      num_layers=8,
      in_channels=100,
      in_latent_channels=512,
      in_tokens=8193,
      out_channels=200,  # mean and variance
      dropout=0,
      use_fp16=False,
      num_heads=16,
      # Parameters for regularization.
      layer_drop=0.1,
      unconditioned_percentage=0.1,  # This implements a mechanism similar to what is used in classifier-free training.
  ):
      super().__init__()
  
      self.in_channels = in_channels
      self.model_channels = model_channels
      self.out_channels = out_channels
      self.dropout = dropout
      self.num_heads = num_heads
      self.unconditioned_percentage = unconditioned_percentage
      self.enable_fp16 = use_fp16
      self.layer_drop = layer_drop
  
      self.inp_block = nn.Conv1d(in_channels, model_channels, 3, 1, 1)
      self.time_embed = nn.Sequential(
          nn.Linear(model_channels, model_channels),
          nn.SiLU(),
          nn.Linear(model_channels, model_channels),
      )
  
      # Either code_converter or latent_converter is used, depending on what type of conditioning data is fed.
      # This model is meant to be able to be trained on both for efficiency purposes - it is far less computationally
      # complex to generate tokens, while generating latents will normally mean propagating through a deep autoregressive
      # transformer network.
      self.code_embedding = nn.Embedding(in_tokens, model_channels)
      self.code_converter = nn.Sequential(
          AttentionBlock(
              model_channels, num_heads, relative_pos_embeddings=True
          ),
          AttentionBlock(
              model_channels, num_heads, relative_pos_embeddings=True
          ),
          AttentionBlock(
              model_channels, num_heads, relative_pos_embeddings=True
          ),
      )
      self.code_norm = normalization(model_channels)
      self.latent_conditioner = nn.Sequential(
          nn.Conv1d(in_latent_channels, model_channels, 3, padding=1),
          AttentionBlock(
              model_channels, num_heads, relative_pos_embeddings=True
          ),
          AttentionBlock(
              model_channels, num_heads, relative_pos_embeddings=True
          ),
          AttentionBlock(
              model_channels, num_heads, relative_pos_embeddings=True
          ),
          AttentionBlock(
              model_channels, num_heads, relative_pos_embeddings=True
          ),
      )
      self.contextual_embedder = nn.Sequential(
          nn.Conv1d(
              in_channels, model_channels, 3, padding=1, stride=2
          ),
          nn.Conv1d(
              model_channels, model_channels * 2, 3, padding=1, stride=2
          ),
          AttentionBlock(
              model_channels * 2,
              num_heads,
              relative_pos_embeddings=True,
              do_checkpoint=False,
          ),
          AttentionBlock(
              model_channels * 2,
              num_heads,
              relative_pos_embeddings=True,
              do_checkpoint=False,
          ),
          AttentionBlock(
              model_channels * 2,
              num_heads,
              relative_pos_embeddings=True,
              do_checkpoint=False,
          ),
          AttentionBlock(
              model_channels * 2,
              num_heads,
              relative_pos_embeddings=True,
              do_checkpoint=False,
          ),
          AttentionBlock(
              model_channels * 2,
              num_heads,
              relative_pos_embeddings=True,
              do_checkpoint=False,
          ),
      )
      self.unconditioned_embedding = nn.Parameter(
          torch.randn(1, model_channels, 1)
      )
      self.conditioning_timestep_integrator = TimestepEmbedSequential(
          DiffusionLayer(model_channels, dropout, num_heads),
          DiffusionLayer(model_channels, dropout, num_heads),
          DiffusionLayer(model_channels, dropout, num_heads),
      )
  
      self.integrating_conv = nn.Conv1d(
          model_channels * 2, model_channels, kernel_size=1
      )
      self.mel_head = nn.Conv1d(
          model_channels, in_channels, kernel_size=3, padding=1
      )
  
      self.layers = nn.ModuleList(
          [
              DiffusionLayer(model_channels, dropout, num_heads)
              for _ in range(num_layers)
          ]
          + [
              ResBlock(
                  model_channels,
                  model_channels,
                  dropout,
                  dims=1,
                  use_scale_shift_norm=True,
              )
              for _ in range(3)
          ]
      )
  
      self.out = nn.Sequential(
          normalization(model_channels),
          nn.SiLU(),
          nn.Conv1d(model_channels, out_channels, 3, padding=1),
      )
  
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 1 to create a new DiffusionTts object from inherited base classes: nn with example: obj = DiffusionTts(model_channels, num_layers, in_channels, in_latent_channels, in_tokens, out_channels, dropout, use_fp16, num_heads, layer_drop, unconditioned_percentage)