this code part 
transform = A.Compose(
            augmentations
            + [
                A.PadIfNeeded(
                    train_image_size[0],
                    train_image_size[1],
                    value=float("nan"),
                    border_mode=cv2.BORDER_CONSTANT,
                ),
            ],
            keypoint_params=cls.get_keypoint_params(),
        )

when calling augs in this config
defaults:
  - _self_
  - dataset: full_4fold
  - augs: flips_light
  - sampler: balanced
  - normalization: default
  - loss: centernet/rfl_bce_mse
  - model: centernet/b5_unet_s2
  - optimizer: adamw
  - scheduler: cos
  - train: default
  - runner: default
  - ema: disabled
  - torch: default

slug: b5_unet_s2
activation: silu
num_channels: ${dataset.num_channels}
num_extra_blocks: 0

model:
  _target_: xview3.centernet.models.centernet_unet.model_factory.create_centernet_unet_model
  model_name: b5_unet_s2
  weights: IMAGENET1K_V1
  pretrained: True
  output_stride: 32
  decoder:
    _target_: xview3.centernet.models.custom_encoder.DebugUNetDecoder
    encoder_channels: [32, 24, 40, 112, 320]
    decoder_channels: [512, 384, 256, 128, 64]
    unet_block:
      _target_: xview3.centernet.models.unet_blocks.UnetBlock
      _partial_: true
    upsample_block:
      _target_: torch.nn.Upsample
      scale_factor: 2
      mode: bilinear
  nas:
    enabled: true
    search_space:
      num_layers: [2, 5]
      num_filters: [64, 128, 256]
      kernel_size: [3, 5]
      activation: [relu, leaky_relu]
      dropout_rate_max: 0.5
    num_trials: 50
  num_channels: 2
  num_classes: 2
  anchors: [[10, 13], [16, 30], [33, 23]]
  input_upsample_factor: 1
  neck_out_channels: 256

  box_coder:
    _target_: xview3.centernet.MultilabelCircleNetCoder
    image_size:
      - 1024
      - 1024
    max_objects: 512
    heatmap_encoding: umich
    labels_encoding: circle
    ignore_value:
      _target_: xview3.ignore_value
    fixed_radius: 3
    labels_radius: 1
    ignore_low_confidence_detections: ${dataset.ignore_low_confidence_detections}

  heads:
    - _target_: xview3.centernet.models.heads.DecoupledHeadGroupNormLateShuffle
      channels: 256
      classifier_dim: 128
      objectness_dim: 128
      size_dim: 128
      offset_dim: 16
      dropout_rate: 0.1
      activation: silu
      num_blocks: 3
    - _target_: xview3.centernet.models.heads.yolo_head.YOLOHead
      num_classes: 2
      anchors: [[10, 13], [16, 30], [33, 23]]
      input_image_size: [640, 640]
      stride: 32

transformer:
  enabled: true
  loss_weight: 1.0

yolo:
  enabled: true
  loss_weight: 1.0

# Training Configuration
train:
  slug: default_training
  epochs: 4
  output_stride: 32
  early_stopping:
    enabled: true
    patience: 2
    mode: "max"  # Options: 'min' for loss-based, 'max' for accuracy-based
    metric: "loss"  # The metric to monitor for early stopping
  image_size: [512, 512]
  batch_size: 16
  num_workers: 4

# Validation Configuration
valid:
  image_size: [2048, 2048]
  batch_size: 16
  num_workers: 4

# Torch Configuration
torch:
  amp:
    enabled: false
  cudnn:
    allow_tf32: True
  cuda_allow_tf32: True
  detect_anomaly: False
  deterministic: False
  benchmark: True

  # Inference and Training Enhancements
  activation_after: "after_model"
  tta: "d4"
  with_offset: True
  yolo_decoders: []

# Hydra Configuration
hydra:
  output_subdir: null
  run:
    dir: .
  sweep:
    dir: .
    subdir: .

# Per-Channel Normalization
normalization:
  channels:
    vv:
      _target_: xview3.SigmoidNormalization
      midpoint: -20.0
      temperature: 0.18
    vh:
      _target_: xview3.SigmoidNormalization
      midpoint: -20.0
      temperature: 0.18
    bathymetry:
      _target_: xview3.CubicRootNormalization
    wind_direction:
      _target_: albumentations.NoOp
    wind_speed:
      _target_: albumentations.NoOp
    mask:
      _target_: albumentations.NoOp

# Dataset Configuration
dataset:
  _target_: xview3.centernet.multilabel_dataset.MultilabelCircleNetDataModule
  data_dir: ${oc.env:XVIEW3_DATA_DIR, "data"}
  normalization:
    mean: [0.485, 0.456, 0.406]
    std: [0.229, 0.224, 0.225]
  slug: full_4fold
  splitter: full  # Ensure 'full' is a valid splitter in your code
  fold: 0
  num_folds: 4
  # Added the following parameters to address references in box_coder
  ignore_low_confidence_detections: true
  ignore_low_confidence_labels: false
  filter_low_confidence_objects: False
  channels:
    - vh
    - vv

# Input Channels Configuration
input_channels:
  - vh
  - vv

# DDP Stuff
local_rank: ${oc.env:LOCAL_RANK, 0}
world_size: ${oc.env:WORLD_SIZE, 0}

experiment: null

seed: 42

deterministic: False
detect_anomaly: False
find_unused: False

transfer: null
checkpoint: null


###this error
(sam3) samerkharboush@Samers-MBP SAR-MPS-OptunaNasFbnYoloTransformers % HYDRA_FULL_ERROR=1 python train_multilabel.py
DEBUG:git.cmd:Popen(['git', 'version'], cwd=/Users/samerkharboush/Downloads/SAR-MPS-OptunaNasFbnYoloTransformers, stdin=None, shell=False, universal_newlines=False)
DEBUG:git.cmd:Popen(['git', 'version'], cwd=/Users/samerkharboush/Downloads/SAR-MPS-OptunaNasFbnYoloTransformers, stdin=None, shell=False, universal_newlines=False)
DEBUG:hydra.core.utils:Setting JobRuntime:name=UNKNOWN_NAME
DEBUG:hydra.core.utils:Setting JobRuntime:name=train_multilabel
Dataset full_4fold
        ignore_low_confidence_detections True
        ignore_low_confidence_labels     True
        filter_low_confidence_objects    False
Train dataset
        Unknown vessels 7788
        Unknown fishing 31133
Valid dataset
        Unknown vessels 36
        Unknown fishing 3934
Error executing job with overrides: []
Traceback (most recent call last):
  File "/Users/samerkharboush/Downloads/SAR-MPS-OptunaNasFbnYoloTransformers/train_multilabel.py", line 499, in <module>
    main()
  File "/opt/anaconda3/envs/sam3/lib/python3.9/site-packages/hydra/main.py", line 94, in decorated_main
    _run_hydra(
  File "/opt/anaconda3/envs/sam3/lib/python3.9/site-packages/hydra/_internal/utils.py", line 394, in _run_hydra
    _run_app(
  File "/opt/anaconda3/envs/sam3/lib/python3.9/site-packages/hydra/_internal/utils.py", line 457, in _run_app
    run_and_report(
  File "/opt/anaconda3/envs/sam3/lib/python3.9/site-packages/hydra/_internal/utils.py", line 223, in run_and_report
    raise ex
  File "/opt/anaconda3/envs/sam3/lib/python3.9/site-packages/hydra/_internal/utils.py", line 220, in run_and_report
    return func()
  File "/opt/anaconda3/envs/sam3/lib/python3.9/site-packages/hydra/_internal/utils.py", line 458, in <lambda>
    lambda: hydra.run(
  File "/opt/anaconda3/envs/sam3/lib/python3.9/site-packages/hydra/_internal/hydra.py", line 132, in run
    _ = ret.return_value
  File "/opt/anaconda3/envs/sam3/lib/python3.9/site-packages/hydra/core/utils.py", line 260, in return_value
    raise self._return_value
  File "/opt/anaconda3/envs/sam3/lib/python3.9/site-packages/hydra/core/utils.py", line 186, in run_job
    ret.return_value = task_function(task_cfg)
  File "/Users/samerkharboush/Downloads/SAR-MPS-OptunaNasFbnYoloTransformers/train_multilabel.py", line 495, in main
    pipeline.train()
  File "/Users/samerkharboush/Downloads/SAR-MPS-OptunaNasFbnYoloTransformers/train_multilabel.py", line 395, in train
    train_loader, valid_loader, _, dataset_callbacks = self.build_datasets(config)
  File "/Users/samerkharboush/Downloads/SAR-MPS-OptunaNasFbnYoloTransformers/train_multilabel.py", line 160, in build_datasets
    train_dataset, train_sampler = dataset_module.get_random_crop_training_dataset(
  File "/Users/samerkharboush/Downloads/SAR-MPS-OptunaNasFbnYoloTransformers/xview3/centernet/multilabel_dataset.py", line 691, in get_random_crop_training_dataset
    augmentations
TypeError: unsupported operand type(s) for +: 'dict' and 'list'
(sam3) samerkharboush@Samers-MBP SAR-MPS-OptunaNasFbnYoloTransformers %