code: def __init__(
      self,
      chin: int = 3,
      hidden: int = 24,
      depth: int = 3,
      growth: float = 2.0,
      max_channels: int = 10_000,
      num_steps: int = 1000,
      emb_all_layers=False,
      cross_attention: bool = False,
      bilstm: bool = False,
      transformer: bool = False,
      codec_dim: tp.Optional[int] = None,
      **kwargs,
  ):
      super().__init__()
      self.encoders = nn.ModuleList()
      self.decoders = nn.ModuleList()
      self.embeddings: tp.Optional[nn.ModuleList] = None
      self.embedding = nn.Embedding(num_steps, hidden)
      if emb_all_layers:
          self.embeddings = nn.ModuleList()
      self.condition_embedding: tp.Optional[nn.Module] = None
      for d in range(depth):
          encoder = EncoderLayer(chin, hidden, **kwargs)
          decoder = DecoderLayer(hidden, chin, **kwargs)
          self.encoders.append(encoder)
          self.decoders.insert(0, decoder)
          if emb_all_layers and d > 0:
              assert self.embeddings is not None
              self.embeddings.append(nn.Embedding(num_steps, hidden))
          chin = hidden
          hidden = min(int(chin * growth), max_channels)
      self.bilstm: tp.Optional[nn.Module]
      if bilstm:
          self.bilstm = BLSTM(chin)
      else:
          self.bilstm = None
      self.use_transformer = transformer
      self.cross_attention = False
      if transformer:
          self.cross_attention = cross_attention
          self.transformer = StreamingTransformer(
              chin,
              8,
              6,
              bias_ff=False,
              bias_attn=False,
              cross_attention=cross_attention,
          )
  
      self.use_codec = False
      if codec_dim is not None:
          self.conv_codec = nn.Conv1d(codec_dim, chin, 1)
          self.use_codec = True
  
imports: from dataclasses import dataclass
  import typing
  import torch
  from torch import nn
  from torch.nn import functional
  from audiocraft.modules.transformer import StreamingTransformer, create_sin_embedding
label_desc: Use image node_id 1 to create a new DiffusionUnet object from inherited base classes: nn with example: obj = DiffusionUnet(chin, hidden, depth, growth, max_channels, num_steps, emb_all_layers, cross_attention, bilstm, transformer, codec_dim)