code: def forward(
      self,
      x: torch.Tensor,
      step: tp.Union[int, torch.Tensor],
      condition: tp.Optional[torch.Tensor] = None,
  ):
      skips = []
      bs = x.size(0)
      z = x
      view_args = [1]
      if type(step) is torch.Tensor:
          step_tensor = step
      else:
          step_tensor = torch.tensor(
              [step], device=x.device, dtype=torch.long
          ).expand(bs)
  
      for idx, encoder in enumerate(self.encoders):
          z = encoder(z)
          if idx == 0:
              z = z + self.embedding(step_tensor).view(
                  bs, -1, *view_args
              ).expand_as(z)
          elif self.embeddings is not None:
              z = z + self.embeddings[idx - 1](step_tensor).view(
                  bs, -1, *view_args
              ).expand_as(z)
  
          skips.append(z)
  
      if self.use_codec:  # insert condition in the bottleneck
          assert (
              condition is not None
          ), "Model defined for conditionnal generation"
          condition_emb = self.conv_codec(
              condition
          )  # reshape to the bottleneck dim
          assert condition_emb.size(-1) <= 2 * z.size(
              -1
          ), f"You are downsampling the conditionning with factor >=2 : {condition_emb.size(-1)=} and {z.size(-1)=}"
          if not self.cross_attention:
              condition_emb = torch.nn.functional.interpolate(
                  condition_emb, z.size(-1)
              )
              assert z.size() == condition_emb.size()
              z += condition_emb
              cross_attention_src = None
          else:
              cross_attention_src = condition_emb.permute(
                  0, 2, 1
              )  # B, T, C
              B, T, C = cross_attention_src.shape
              positions = torch.arange(T, device=x.device).view(
                  1, -1, 1
              )
              pos_emb = create_sin_embedding(
                  positions,
                  C,
                  max_period=10_000,
                  dtype=cross_attention_src.dtype,
              )
              cross_attention_src = cross_attention_src + pos_emb
      if self.use_transformer:
          z = self.transformer(
              z.permute(0, 2, 1),
              cross_attention_src=cross_attention_src,
          ).permute(0, 2, 1)
      else:
          if self.bilstm is None:
              z = torch.zeros_like(z)
          else:
              z = self.bilstm(z)
  
      for decoder in self.decoders:
          s = skips.pop(-1)
          z = z[:, :, : s.shape[2]]
          z = z + s
          z = decoder(z)
  
      z = z[:, :, : x.shape[2]]
      return Output(z)
  
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 2 for calling the DiffusionUnet obj's underlying member method code with example usage: obj.forward(x, step, condition) and returns: Output