code: def forward(self, z, **kwargs):
      # assert z.shape[1:] == self.z_shape[1:]
      self.last_z_shape = z.shape
  
      # timestep embedding
      temb = None
  
      # z to block_in
      h = self.conv_in(z)
  
      # middle
      h = self.mid.block_1(h, temb, **kwargs)
      h = self.mid.attn_1(h, **kwargs)
      h = self.mid.block_2(h, temb, **kwargs)
  
      # upsampling
      for i_level in reversed(range(self.num_resolutions)):
          for i_block in range(self.num_res_blocks + 1):
              h = self.up[i_level].block[i_block](h, temb, **kwargs)
              if len(self.up[i_level].attn) > 0:
                  h = self.up[i_level].attn[i_block](h, **kwargs)
          if i_level != 0:
              h = self.up[i_level].upsample(h)
  
      # end
      if self.give_pre_end:
          return h
  
      h = self.norm_out(h)
      h = nonlinearity(h)
      h = self.conv_out(h, **kwargs)
      if self.tanh_out:
          h = torch.tanh(h)
      return h
  
imports: import logging
  import math
  from typing import Any, Callable, Optional
  import numpy
  import torch
  import torch.nn
  from einops import rearrange
  from packaging import version
  from ...modules.attention import LinearAttention, MemoryEfficientCrossAttention
label_desc: Use image node_id 6 for calling the Decoder obj's underlying member method code with example usage: obj.forward(z) and returns: h, h