code: def movq_resnet_to_diffusers_checkpoint_spatial_norm(
      resnet, checkpoint, *, diffusers_resnet_prefix, resnet_prefix
  ):
      rv = {
          # norm1
          f"{diffusers_resnet_prefix}.norm1.norm_layer.weight": checkpoint[
              f"{resnet_prefix}.norm1.norm_layer.weight"
          ],
          f"{diffusers_resnet_prefix}.norm1.norm_layer.bias": checkpoint[
              f"{resnet_prefix}.norm1.norm_layer.bias"
          ],
          f"{diffusers_resnet_prefix}.norm1.conv_y.weight": checkpoint[
              f"{resnet_prefix}.norm1.conv_y.weight"
          ],
          f"{diffusers_resnet_prefix}.norm1.conv_y.bias": checkpoint[
              f"{resnet_prefix}.norm1.conv_y.bias"
          ],
          f"{diffusers_resnet_prefix}.norm1.conv_b.weight": checkpoint[
              f"{resnet_prefix}.norm1.conv_b.weight"
          ],
          f"{diffusers_resnet_prefix}.norm1.conv_b.bias": checkpoint[
              f"{resnet_prefix}.norm1.conv_b.bias"
          ],
          # conv1
          f"{diffusers_resnet_prefix}.conv1.weight": checkpoint[
              f"{resnet_prefix}.conv1.weight"
          ],
          f"{diffusers_resnet_prefix}.conv1.bias": checkpoint[
              f"{resnet_prefix}.conv1.bias"
          ],
          # norm2
          f"{diffusers_resnet_prefix}.norm2.norm_layer.weight": checkpoint[
              f"{resnet_prefix}.norm2.norm_layer.weight"
          ],
          f"{diffusers_resnet_prefix}.norm2.norm_layer.bias": checkpoint[
              f"{resnet_prefix}.norm2.norm_layer.bias"
          ],
          f"{diffusers_resnet_prefix}.norm2.conv_y.weight": checkpoint[
              f"{resnet_prefix}.norm2.conv_y.weight"
          ],
          f"{diffusers_resnet_prefix}.norm2.conv_y.bias": checkpoint[
              f"{resnet_prefix}.norm2.conv_y.bias"
          ],
          f"{diffusers_resnet_prefix}.norm2.conv_b.weight": checkpoint[
              f"{resnet_prefix}.norm2.conv_b.weight"
          ],
          f"{diffusers_resnet_prefix}.norm2.conv_b.bias": checkpoint[
              f"{resnet_prefix}.norm2.conv_b.bias"
          ],
          # conv2
          f"{diffusers_resnet_prefix}.conv2.weight": checkpoint[
              f"{resnet_prefix}.conv2.weight"
          ],
          f"{diffusers_resnet_prefix}.conv2.bias": checkpoint[
              f"{resnet_prefix}.conv2.bias"
          ],
      }
  
      if resnet.conv_shortcut is not None:
          rv.update(
              {
                  f"{diffusers_resnet_prefix}.conv_shortcut.weight": checkpoint[
                      f"{resnet_prefix}.nin_shortcut.weight"
                  ],
                  f"{diffusers_resnet_prefix}.conv_shortcut.bias": checkpoint[
                      f"{resnet_prefix}.nin_shortcut.bias"
                  ],
              }
          )
  
      return rv
  
imports: import argparse
  import os
  import tempfile
  import torch
  from accelerate import load_checkpoint_and_dispatch
  from diffusers import UNet2DConditionModel
  from diffusers.models.prior_transformer import PriorTransformer
  from diffusers.models.vq_model import VQModel
label_desc: Use image node_id 28 for calling a global function with example usage: movq_resnet_to_diffusers_checkpoint_spatial_norm(resnet, checkpoint) and returns: rv