the model integration of using NAS, OPTUNA, FBN, FAN, SwinTransformer with CenterNetUnet needs to be reviewed deeply here in both files centernet_unet.py and multilabel_circle_net.yaml
@centernet_unet.py @multilabel_circle_net.yaml 

here are both deeply as you are a super advanced machine learning software engineer please help to solve errors and creatively enhance, and ensure deep entergation of these models in that project of the current codebase which able to detect vessels and decide if fishing or not using TIFF SAR images @Codebase 

if you understand my model say 'done' so I can start show you the files after just reading the error

##the error
DEBUG:rasterio._env:GDAL_DATA found in environment.
DEBUG:rasterio._env:PROJ_DATA found in environment.
DEBUG:rasterio._env:Started GDALEnv: self=<rasterio._env.GDALEnv object at 0x3359740d0>.
DEBUG:rasterio.env:Entered env context: <rasterio.env.Env object at 0x3359740a0>
DEBUG:rasterio._base:Sharing flag: 0
DEBUG:rasterio._base:Nodata success: 1, Nodata value: -32768.000000
DEBUG:rasterio._base:Dataset <open DatasetReader name='/Volumes/Crucial/data/full/train/8a6781613364d055t/VV_dB.tif' mode='r'> is started.
DEBUG:rasterio.env:Exiting env context: <rasterio.env.Env object at 0x3359740a0>
DEBUG:rasterio.env:Cleared existing <rasterio._env.GDALEnv object at 0x3359740d0> options
DEBUG:rasterio._env:Stopped GDALEnv <rasterio._env.GDALEnv object at 0x3359740d0>.
DEBUG:rasterio.env:Exiting outermost env
DEBUG:rasterio.env:Exited env context: <rasterio.env.Env object at 0x3359740a0>
DEBUG:rasterio.env:Entering env context: <rasterio.env.Env object at 0x331e4b7c0>
DEBUG:rasterio.env:Starting outermost env
DEBUG:rasterio.env:No GDAL environment exists
DEBUG:rasterio.env:New GDAL environment <rasterio._env.GDALEnv object at 0x3359740d0> created
DEBUG:rasterio._env:GDAL_DATA found in environment.
DEBUG:rasterio._env:PROJ_DATA found in environment.
DEBUG:rasterio._env:Started GDALEnv: self=<rasterio._env.GDALEnv object at 0x3359740d0>.
DEBUG:rasterio.env:Entered env context: <rasterio.env.Env object at 0x331e4b7c0>
DEBUG:rasterio._io:Output nodata value read from file: -32768.0
DEBUG:rasterio._io:Output nodata values: [-32768.0]
DEBUG:rasterio._io:all_valid: False
DEBUG:rasterio._io:mask_flags: ([<MaskFlags.nodata: 8>],)
DEBUG:rasterio._io:Jump straight to _read()
DEBUG:rasterio._io:Window: Window(col_off=21276, row_off=5900, width=512, height=512)
DEBUG:rasterio._io:IO window xoff=21276.0 yoff=5900.0 width=512.0 height=512.0
DEBUG:rasterio.env:Exiting env context: <rasterio.env.Env object at 0x3377b4c70>
DEBUG:rasterio.env:Cleared existing <rasterio._env.GDALEnv object at 0x3377b4b50> options
DEBUG:rasterio._env:Stopped GDALEnv <rasterio._env.GDALEnv object at 0x3377b4b50>.
DEBUG:rasterio.env:Exiting outermost env
DEBUG:rasterio.env:Exited env context: <rasterio.env.Env object at 0x3377b4c70>
[DEBUG] Channel 0 shape: (512, 512)
[DEBUG] Channel 1 shape: (512, 512)
(256.0, 268.0, 0.0) [<class 'float'>, <class 'float'>, <class 'float'>]
DEBUG:rasterio.env:Exiting env context: <rasterio.env.Env object at 0x331e4b7c0>
DEBUG:rasterio.env:Cleared existing <rasterio._env.GDALEnv object at 0x3359740d0> options
DEBUG:rasterio._env:Stopped GDALEnv <rasterio._env.GDALEnv object at 0x3359740d0>.
DEBUG:rasterio.env:Exiting outermost env
DEBUG:rasterio.env:Exited env context: <rasterio.env.Env object at 0x331e4b7c0>
[DEBUG] Channel 0 shape: (512, 512)
[DEBUG] Channel 1 shape: (512, 512)
(297.0, 256.0, 2.499000072479248) [<class 'float'>, <class 'float'>, <class 'float'>]
Available keys in model config: dict_keys(['_target_', 'slug', 'weights', 'pretrained', 'output_stride', 'image_size', 'window_size', 'transformer', 'decoder', 'encoder', 'nas', 'num_channels', 'num_classes', 'anchors', 'input_upsample_factor', 'neck_out_channels', 'box_coder', 'head', 'config'])
Complete model configuration:
 _target_: xview3.centernet.models.CenterNetUNetModel.from_config
slug: b5_unet_s2
weights: IMAGENET1K_V1
pretrained: true
output_stride: 32
image_size:
- 1024
- 1024
window_size:
- 4
- 4
transformer:
  enabled: true
  embed_dim: 96
  depths:
  - 2
  - 2
  - 6
  - 2
  num_heads:
  - 3
  - 6
  - 12
  - 24
  window_size:
  - 4
  - 4
  mlp_ratio: 4.0
  qkv_bias: true
  qk_scale: null
  drop_rate: 0.0
  attn_drop_rate: 0.0
  drop_path_rate: 0.1
  norm_layer: null
  use_dropout: false
  use_custom_pos_embed: false
decoder:
  _target_: xview3.centernet.models.custom_encoder.DebugUNetDecoder
  encoder_channels:
  - 32
  - 24
  - 40
  - 112
  - 320
  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
encoder:
  _target_: pytorch_toolbelt.modules.encoders.TimmEfficientNetV2
  model_name: efficientnetv2_rw_s
  pretrained: true
  activation: silu
  drop_rate: 0.1
  drop_path_rate: 0.1
  layers:
  - 1
  - 2
  - 3
  - 4
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.bboxer.multilabel_circle_coder.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}
head:
  decoupled_head:
    _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
  yolo_head:
    _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
config:
  slug: b5_unet_s2
  activation: silu
  num_channels: ${dataset.num_channels}
  num_extra_blocks: 0
  encoder:
    _target_: pytorch_toolbelt.modules.encoders.TimmB5Encoder
    pretrained: true
    activation: silu
    layers:
    - 1
    - 2
    - 3
    - 4
  decoder:
    upsample_type: shuffle
    block_type: IRBlock
    channels:
    - 256
    - 384
    - 512
  head:
    _target_: xview3.centernet.models.heads.DecoupledHeadGroupNormLateShuffle
    classifier_dim: 128
    objectness_dim: 128
    size_dim: 64
    offset_dim: 16
    dropout_rate: 0.1
    activation: silu
    num_groups: 4
    num_blocks: 3
  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}

[DEBUG] Number of heads instantiated: 2
[DEBUG] Head 0 type: <class 'xview3.centernet.models.heads.decoupled_head.DecoupledHeadGroupNormLateShuffle'>
[DEBUG] Head 1 type: <class 'xview3.centernet.models.heads.yolo_head.YOLOHead'>
[2024-10-05 12:54:09,358][timm.models._builder][INFO] - Loading pretrained weights from Hugging Face hub (timm/efficientnetv2_rw_s.ra2_in1k)
[2024-10-05 12:54:09,528][timm.models._hub][INFO] - [timm/efficientnetv2_rw_s.ra2_in1k] Safe alternative available for 'pytorch_model.bin' (as 'model.safetensors'). Loading weights using safetensors.
[2024-10-05 12:54:09,566][timm.models._builder][WARNING] - Unexpected keys (bn2.bias, bn2.num_batches_tracked, bn2.running_mean, bn2.running_var, bn2.weight, classifier.bias, classifier.weight, conv_head.weight) found while loading pretrained weights. This may be expected if model is being adapted.
[2024-10-05 12:54:09,813][timm.models._builder][INFO] - Loading pretrained weights from Hugging Face hub (timm/efficientnetv2_rw_s.ra2_in1k)
[2024-10-05 12:54:09,951][timm.models._hub][INFO] - [timm/efficientnetv2_rw_s.ra2_in1k] Safe alternative available for 'pytorch_model.bin' (as 'model.safetensors'). Loading weights using safetensors.
[2024-10-05 12:54:09,972][timm.models._builder][INFO] - Converted input conv conv_stem pretrained weights from 3 to 2 channel(s)
[2024-10-05 12:54:09,986][timm.models._builder][WARNING] - Unexpected keys (bn2.bias, bn2.num_batches_tracked, bn2.running_mean, bn2.running_var, bn2.weight, classifier.bias, classifier.weight, conv_head.weight) found while loading pretrained weights. This may be expected if model is being adapted.
Error executing job with overrides: []
Traceback (most recent call last):
  File "/Volumes/Crucial/SAR-MPS-OptunaNasFbnYoloTransformers/train_multilabel3.py", line 806, 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 "/Volumes/Crucial/SAR-MPS-OptunaNasFbnYoloTransformers/train_multilabel3.py", line 802, in main
    pipeline.train()
  File "/Volumes/Crucial/SAR-MPS-OptunaNasFbnYoloTransformers/train_multilabel3.py", line 520, in train
    self.model = self.build_model(config, train_loader)
  File "/Volumes/Crucial/SAR-MPS-OptunaNasFbnYoloTransformers/train_multilabel3.py", line 331, in build_model
    self.model = CenterNetUNetModel.from_config(config.model)
  File "/Volumes/Crucial/SAR-MPS-OptunaNasFbnYoloTransformers/xview3/centernet/models/centernet_unet.py", line 566, in from_config
    encoder_module = CustomTimmEncoder(
  File "/Volumes/Crucial/SAR-MPS-OptunaNasFbnYoloTransformers/xview3/centernet/models/custom_encoder.py", line 139, in __init__
    self.conv_stem = self.patch_embed.proj
  File "/opt/anaconda3/envs/sam3/lib/python3.9/site-packages/torch/nn/modules/module.py", line 1729, in __getattr__
    raise AttributeError(f"'{type(self).__name__}' object has no attribute '{name}'")
AttributeError: 'CustomTimmEncoder' object has no attribute 'patch_embed'
(sam3) samerkharboush@Samers-MBP SAR-MPS-Optun