code: def forward(self, x):
      # timestep embedding
      temb = None
  
      # downsampling
      hs = [self.conv_in(x)]
      for i_level in range(self.num_resolutions):
          for i_block in range(self.num_res_blocks):
              h = self.down[i_level].block[i_block](hs[-1], temb)
              if len(self.down[i_level].attn) > 0:
                  h = self.down[i_level].attn[i_block](h)
              hs.append(h)
          if i_level != self.num_resolutions - 1:
              hs.append(self.down[i_level].downsample(hs[-1]))
  
      # middle
      h = hs[-1]
      h = self.mid.block_1(h, temb)
      h = self.mid.attn_1(h)
      h = self.mid.block_2(h, temb)
  
      # end
      h = self.norm_out(h)
      h = nonlinearity(h)
      h = self.conv_out(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 2 for calling the Encoder obj's underlying member method code with example usage: obj.forward(x) and returns: h