code: def forward(self, t, x):
      t = self.mlp(self.time_pos_emb(t))
  
      hs = [self.first_conv(x)]
      for i, (res_blocks, attn_blocks, spatial_blocks) in enumerate(
          zip(
              self.down_res_blocks,
              self.down_attn_blocks,
              self.down_spatial_blocks,
          )
      ):
          if len(attn_blocks) > 0:
              for res_block, attn_block in zip(res_blocks, attn_blocks):
                  h = res_block(hs[-1], t)
                  h = attn_block(h)
                  hs.append(h)
          else:
              for res_block in res_blocks:
                  h = res_block(hs[-1], t)
                  hs.append(h)
          if len(spatial_blocks) > 0:
              (spatial_block,) = spatial_blocks
              hs.append(spatial_block(hs[-1]))
  
      h = hs[-1]
      h = self.mid_block1(h, t)
      h = self.mid_attn(h)
      h = self.mid_block2(h, t)
  
      for i, (res_blocks, attn_blocks, spatial_blocks) in enumerate(
          zip(
              self.ups_res_blocks,
              self.ups_attn_blocks,
              self.ups_spatial_blocks,
          )
      ):
          if len(attn_blocks) > 0:
              for res_block, attn_block in zip(res_blocks, attn_blocks):
                  h = res_block(torch.cat((h, hs.pop()), dim=1), t)
                  h = attn_block(h)
          else:
              for res_block in res_blocks:
                  h = res_block(torch.cat((h, hs.pop()), dim=1), t)
          if len(spatial_blocks) > 0:
              (spatial_block,) = spatial_blocks
              h = spatial_block(h)
      return self.final_conv(h)
  
imports: import math
  import kornia
  import torch
  import torch.nn.functional
  from einops import rearrange
  from torch import nn
label_desc: Use image node_id 2 for calling the Unet obj's underlying member method code with example usage: obj.forward(t, x) and returns: self