class_docstr: Mix vision Transformer Model.
code: def __init__(self, pre_process=True, post_process=True):
      super(MitInpaintingModel, self).__init__()
      self.pre_process = pre_process
      self.post_process = post_process
  
      args = get_args()
      self.patch_dim = args.patch_dim
      self.img_h = args.img_h
      self.img_w = args.img_w
      self.flatten_dim = self.patch_dim * self.patch_dim * 3
      self.backbone = mit_b3()
  
      self.in_channels = [64, 128, 320, 512]
      self.embedding_dim = 768
  
      (
          c1_in_channels,
          c2_in_channels,
          c3_in_channels,
          c4_in_channels,
      ) = self.in_channels
  
      self.linear_c4 = MLP(
          input_dim=c4_in_channels, embed_dim=self.embedding_dim
      )
      self.linear_c3 = MLP(
          input_dim=c3_in_channels, embed_dim=self.embedding_dim
      )
      self.linear_c2 = MLP(
          input_dim=c2_in_channels, embed_dim=self.embedding_dim
      )
      self.linear_c1 = MLP(
          input_dim=c1_in_channels, embed_dim=self.embedding_dim
      )
  
      self.conv_fuse = torch.nn.Conv2d(
          self.embedding_dim * 4, self.embedding_dim, 1, 1, bias=False
      )
      self.norm = apex.parallel.SyncBatchNorm(self.embedding_dim)
      self.dropout = torch.nn.Dropout2d(0.1)
  
      self.linear_pred = torch.nn.Conv2d(
          self.embedding_dim, self.flatten_dim, kernel_size=1
      )
  
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 1 to create a new MitInpaintingModel object from inherited base classes: MegatronModule with example: obj = MitInpaintingModel(pre_process, post_process)