code: def forward(self, input):
      hidden_states = self.backbone(input)
  
      if not self.post_process:
          return hidden_states
      decoded_output = self.linear_decoder(hidden_states)
      output = einops.rearrange(
          decoded_output,
          "b (h w) (p1 p2 c) -> b c (h p1) (w p2)",
          p1=self.patch_dim,
          p2=self.patch_dim,
          h=self.img_h // self.patch_dim,
          w=self.img_w // self.patch_dim,
      )
  
      return output
  
imports: import math
  import apex
  import einops
  import torch
  import torch.nn.functional
  from megatron import get_args, print_rank_0
  from megatron.model.utils import get_linear_layer
  from megatron.model.vision.vit_backbone import VitBackbone
  from megatron.model.module import MegatronModule
  from megatron.model.vision.mit_backbone import mit_b3
  from megatron.model.vision.utils import resize
label_desc: Use image node_id 3 for calling the VitInpaintingModel obj's underlying member method code with example usage: obj.forward(input) and returns: output, hidden_states