code: def forward(self, x, temb):
      h = x
      h = self.norm1(h)
      h = nonlinearity(h)
      h = self.conv1(h)
  
      if temb is not None:
          h = h + self.temb_proj(nonlinearity(temb))[:, :, None, None]
  
      h = self.norm2(h)
      h = nonlinearity(h)
      h = self.dropout(h)
      h = self.conv2(h)
  
      if self.in_channels != self.out_channels:
          if self.use_conv_shortcut:
              x = self.conv_shortcut(x)
          else:
              x = self.nin_shortcut(x)
  
      return x + 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 ResnetBlock obj's underlying member method code with example usage: obj.forward(x, temb) and returns: unknown