text: # model settings
  norm_cfg = dict(type='SyncBN', requires_grad=True)
  model = dict(
      type='EncoderDecoder',
      pretrained=None,
      backbone=dict(
          type='LIT',
          embed_dim=96,
          depths=[2, 2, 6, 2],
          num_heads=[3, 6, 12, 24],
          window_size=7,
          mlp_ratio=4.,
          qkv_bias=True,
          qk_scale=None,
          drop_rate=0.,
          attn_drop_rate=0.,
          drop_path_rate=0.2,
          ape=False,
          patch_norm=True,
          out_indices=(0, 1, 2, 3),
          use_checkpoint=False),
      neck=dict(
          type='FPN',
          in_channels=[256, 512, 1024, 2048],
          out_channels=256,
          num_outs=4),
      decode_head=dict(
          type='FPNHead',
          in_channels=[256, 256, 256, 256],
          in_index=[0, 1, 2, 3],
          feature_strides=[4, 8, 16, 32],
          channels=128,
          dropout_ratio=0.1,
          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'))
  