code: def forward(self, x, r, c):
      x_in = x
      x = self.projection(x)
      c_embed = self.cond_mapper(c)
      r_embed = self.gen_r_embedding(r)
  
      if self.training and self.gradient_checkpointing:
  
          def create_custom_forward(module):
              def custom_forward(*inputs):
                  return module(*inputs)
  
              return custom_forward
  
          if is_torch_version(">=", "1.11.0"):
              for block in self.blocks:
                  if isinstance(block, AttnBlock):
                      x = torch.utils.checkpoint.checkpoint(
                          create_custom_forward(block),
                          x,
                          c_embed,
                          use_reentrant=False,
                      )
                  elif isinstance(block, TimestepBlock):
                      x = torch.utils.checkpoint.checkpoint(
                          create_custom_forward(block),
                          x,
                          r_embed,
                          use_reentrant=False,
                      )
                  else:
                      x = torch.utils.checkpoint.checkpoint(
                          create_custom_forward(block),
                          x,
                          use_reentrant=False,
                      )
          else:
              for block in self.blocks:
                  if isinstance(block, AttnBlock):
                      x = torch.utils.checkpoint.checkpoint(
                          create_custom_forward(block), x, c_embed
                      )
                  elif isinstance(block, TimestepBlock):
                      x = torch.utils.checkpoint.checkpoint(
                          create_custom_forward(block), x, r_embed
                      )
                  else:
                      x = torch.utils.checkpoint.checkpoint(
                          create_custom_forward(block), x
                      )
      else:
          for block in self.blocks:
              if isinstance(block, AttnBlock):
                  x = block(x, c_embed)
              elif isinstance(block, TimestepBlock):
                  x = block(x, r_embed)
              else:
                  x = block(x)
      a, b = self.out(x).chunk(2, dim=1)
      return (x_in - a) / ((1 - b).abs() + 1e-5)
  
imports: import math
  from typing import Dict, Union
  import torch
  import torch.nn
  from ...configuration_utils import ConfigMixin, register_to_config
  from ...loaders import PeftAdapterMixin, UNet2DConditionLoadersMixin
  from ...models.attention_processor import ADDED_KV_ATTENTION_PROCESSORS, CROSS_ATTENTION_PROCESSORS, AttentionProcessor, AttnAddedKVProcessor, AttnProcessor
  from ...models.lora import LoRACompatibleConv, LoRACompatibleLinear
  from ...models.modeling_utils import ModelMixin
  from ...utils import USE_PEFT_BACKEND, is_torch_version
  from .modeling_wuerstchen_common import AttnBlock, ResBlock, TimestepBlock, WuerstchenLayerNorm
label_desc: Use image node_id 7 for calling the WuerstchenPrior obj's underlying member method code with example usage: obj.forward(x, r, c) and returns: unknown, custom_forward, module