text: _base_ = [
      '../../_base_/meta_test/cub_meta-test_5way-1shot.py',
      '../../_base_/runtime/epoch_based_runtime.py',
      '../../_base_/schedules/sgd_200epoch.py'
  ]
  
  img_size = 84
  img_norm_cfg = dict(
      mean=[123.675, 116.28, 103.53], std=[58.395, 57.12, 57.375], to_rgb=True)
  train_pipeline = [
      dict(type='LoadImageFromFile'),
      dict(type='RandomResizedCrop', size=img_size),
      dict(type='RandomFlip', flip_prob=0.5, direction='horizontal'),
      dict(type='ColorJitter', brightness=0.4, contrast=0.4, saturation=0.4),
      dict(type='Normalize', **img_norm_cfg),
      dict(type='ImageToTensor', keys=['img']),
      dict(type='ToTensor', keys=['gt_label']),
      dict(type='Collect', keys=['img', 'gt_label'])
  ]
  
  meta_finetune_cfg = dict(
      num_steps=150,
      optimizer=dict(
          type='SGD', lr=0.01, momentum=0.9, dampening=0.9, weight_decay=0.001))
  
  data = dict(
      samples_per_gpu=64,
      workers_per_gpu=8,
      train=dict(
          type='CUBDataset',
          data_prefix='data/CUB_200_2011',
          subset='train',
          pipeline=train_pipeline),
      val=dict(
          meta_test_cfg=dict(
              fast_test=True,
              support=dict(
                  batch_size=4, drop_last=True, train=meta_finetune_cfg))),
      test=dict(
          meta_test_cfg=dict(
              fast_test=True,
              support=dict(
                  batch_size=4, drop_last=True, train=meta_finetune_cfg))))
  
  model = dict(
      type='Baseline',
      backbone=dict(type='Conv4'),
      head=dict(type='LinearHead', num_classes=100, in_channels=1600),
      meta_test_head=dict(type='LinearHead', num_classes=5, in_channels=1600))
  