class_docstr: Mix vision Transformer Model.
code: def forward(self, input):
      c1, c2, c3, c4 = self.backbone(input)
  
      n, _, h, w = c4.shape
      _c4 = (
          self.linear_c4(c4)
          .permute(0, 2, 1)
          .reshape(n, -1, c4.shape[2], c4.shape[3])
      )
      _c4 = resize(
          _c4, size=c1.size()[2:], mode="bilinear", align_corners=False
      )
  
      _c3 = (
          self.linear_c3(c3)
          .permute(0, 2, 1)
          .reshape(n, -1, c3.shape[2], c3.shape[3])
      )
      _c3 = resize(
          _c3, size=c1.size()[2:], mode="bilinear", align_corners=False
      )
  
      _c2 = (
          self.linear_c2(c2)
          .permute(0, 2, 1)
          .reshape(n, -1, c2.shape[2], c2.shape[3])
      )
      _c2 = resize(
          _c2, size=c1.size()[2:], mode="bilinear", align_corners=False
      )
  
      _c1 = (
          self.linear_c1(c1)
          .permute(0, 2, 1)
          .reshape(n, -1, c1.shape[2], c1.shape[3])
      )
  
      _c = torch.cat([_c4, _c3, _c2, _c1], dim=1)
      _c = self.conv_fuse(_c)
  
      x = self.norm(_c)
      x = F.relu(x, inplace=True)
      x = self.dropout(x)
  
      x = self.linear_pred(x)
  
      output = einops.rearrange(
          x,
          "b (c p1 p2) h w -> 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 MitInpaintingModel obj's underlying member method code with example usage: obj.forward(input) and returns: output