text: # model settings
  norm_cfg = dict(type='SyncBN', requires_grad=True)
  model = dict(
      type='EncoderDecoder',
      backbone=dict(
          type='VIT_MLA_ConvFuse_TwoLayer',
          model_name='deit_base_distilled_path16_384',
          img_size=768,
          patch_size=16, 
          in_chans=3,
          embed_dim=1024,
          depth=24,
          num_heads=16, 
          num_classes=19,
          drop_rate=0.1,
          norm_cfg=norm_cfg,
          pos_embed_interp=True,
          align_corners=False,
          mla_channels=256,
          mla_index=(5,11,17,23)
          ),
      decode_head=dict(
          type='VIT_MLALAConvFuseTwoLayerHead',
          in_channels=1024,
          channels=512,
          img_size=768,
          mla_channels=256,
          mlahead_channels=128,
          num_classes=19,
          norm_cfg=norm_cfg,
          align_corners=False,
          loss_decode=dict(
              type='CrossEntropyLoss', use_sigmoid=False, loss_weight=1.0)))
  # model training and testing settings
  train_cfg = dict()
  test_cfg = dict(mode='whole')
  
  
  