Following this instruction :
1. Strictly adhere to PEP-8 standards, ensuring code is clean, well-structured, and follows Python's style guide for readability.
2. Use appropriate modules like `typing` for type hints, ensuring code clarity, maintainability, and leveraging Python's standard libraries effectively.
3. Focus on time, space, robustness, scalability; write optimized, efficient code that can handle large datasets and scale seamlessly.
4. Implement comprehensive error handling, catching exceptions gracefully, providing meaningful error messages, and ensuring code robustness under unexpected conditions.
5. Ensure the developer agent writes advanced, maintainable Python code, balancing performance and readability while adhering to best practices.



TASK : we have code make professional code : below looking not novel way: make novel 


from typing import Any, Dict, List, Type,Tuple
import sys
import logging

from defences import (
    EvasionDetector,
    BinaryInputDetector,
    BinaryActivationDetector,
    SubsetScanningDetector,
    PoisonFilteringDefence,
    GroundTruthEvaluator,
    ActivationDefence,
    ClusteringAnalyzer,
    ProvenanceDefense,
    RONIDefense,
    SpectralSignatureDefense,
    ClassLabels,
    GaussianNoise,
    HighConfidence,
    Postprocessor,
    ReverseSigmoid,
    Rounded,
    CutMix,
    CutMixPyTorch,
    CutMixTensorFlowV2,
    Cutout,
    CutoutPyTorch,
    CutoutTensorFlowV2,
    FeatureSqueezing,
    GaussianAugmentation,
    DefenseGAN,
    InverseGAN,
    JpegCompression,
    LabelSmoothing,
    Mixup,
    MixupPyTorch,
    MixupTensorFlowV2,
    Mp3Compression,
    Mp3CompressionPyTorch,
    PixelDefend,
    Preprocessor,
    Resample,
    SpatialSmoothing,
    SpatialSmoothingPyTorch,
    SpatialSmoothingTensorFlowV2,
    ThermometerEncoding,
    TotalVarMin,
    VideoCompression,
    VideoCompressionPyTorch,
    Trainer,
    AdversarialTrainer,
    AdversarialTrainerCertifiedPytorch,
    AdversarialTrainerCertifiedIBPPyTorch,
    AdversarialTrainerMadryPGD,
    AdversarialTrainerFBF,
    AdversarialTrainerFBFPyTorch,
    AdversarialTrainerTRADES,
    AdversarialTrainerTRADESPyTorch,
    AdversarialTrainerAWP,
    AdversarialTrainerAWPPyTorch,
    AdversarialTrainerOAAT,
    AdversarialTrainerOAATPyTorch,
    DPInstaHideTrainer,
    Transformer,
    evasion,
    poisoning
)

logging.basicConfig(
    level=logging.INFO,
    format="%(asctime)s [%(levelname)s] %(message)s",
    handlers=[logging.StreamHandler(sys.stdout)],
)
logger = logging.getLogger(__name__)


DefenseType = Type[Any]
Custom_Clip_Values:tuple[int,int]=(0.0, 1.0)
Num_Classes:int=10
Length:int=10
Original_Sample_Rate:int=48000
New_Sample_Rate:int=16000

def get_defense_list() -> List[DefenseType]:
    """
    Retrieve a list of defense instances from the ART library.

    Returns:
        List[DefenseType]: A list of instantiated defense classes.
    """
    try:
        clip_values = (0.0, 1.0)  # Adjust based on preprocessing

        defense_list = [
             EvasionDetector,
           BinaryInputDetector,
           BinaryActivationDetector,
           SubsetScanningDetector,
           PoisonFilteringDefence,
           GroundTruthEvaluator,
           ActivationDefence,
           ClusteringAnalyzer,
           ProvenanceDefense,
           RONIDefense,
           SpectralSignatureDefense,
           ClassLabels,
           GaussianNoise,
           HighConfidence,
           Postprocessor,
           ReverseSigmoid,
           Rounded,
           CutMix,
           CutMixPyTorch,
           CutMixTensorFlowV2,
           Cutout,
           CutoutPyTorch,
           CutoutTensorFlowV2,
           FeatureSqueezing,
           GaussianAugmentation,
           DefenseGAN,
           InverseGAN,
           JpegCompression,
           LabelSmoothing,
           Mixup,
           MixupPyTorch,
           MixupTensorFlowV2,
           Mp3Compression,
           Mp3CompressionPyTorch,
           PixelDefend,
           Preprocessor,
           Resample,
           SpatialSmoothing,
           SpatialSmoothingPyTorch,
           SpatialSmoothingTensorFlowV2,
           ThermometerEncoding,
           TotalVarMin,
           VideoCompression,
           VideoCompressionPyTorch,
           Trainer,
           AdversarialTrainer,
           AdversarialTrainerCertifiedPytorch,
           AdversarialTrainerCertifiedIBPPyTorch,
           AdversarialTrainerMadryPGD,
           AdversarialTrainerFBF,
           AdversarialTrainerFBFPyTorch,
           AdversarialTrainerTRADES,
           AdversarialTrainerTRADESPyTorch,
           AdversarialTrainerAWP,
           AdversarialTrainerAWPPyTorch,
           AdversarialTrainerOAAT,
           AdversarialTrainerOAATPyTorch,
           DPInstaHideTrainer,
           Transformer,
           evasion,
           poisoning
        ]

        logger.info("Defense list created successfully.")
        return defense_list

    except Exception as e:
        logger.error(f"Error creating defense list: {e}")
        raise

def get_defense_parameters(defense: DefenseType) -> Dict[str, Any]:
    """
    Retrieve default parameters for each defense.

    Args:
        defense (DefenseType): The defense instance.

    Returns:
        Dict[str, Any]: Dictionary of parameters.
    """
    try:
        default_params: Dict[Type[Any], Dict[str, Any]] = {
            JpegCompression: {
            "clip_values":Custom_Clip_Values,
            "quality": 50,
            "channels_first":  False,
            "apply_fit":  True,
            "apply_predict":  True,
            "verbose": False,
            },
            FeatureSqueezing: {
            "clip_values": Custom_Clip_Values,
            "bit_depth":8,
            "apply_fit":  False,
            "apply_predict": True,
            },
            GaussianNoise: {
            "scale":  0.2,
            "apply_fit": False, 
            "apply_predict": True
            },
            LabelSmoothing: {
             "max_value":  0.9,
             "apply_fit": True,
             "apply_predict": False,
            },
            PixelDefend: {
               "clip_values": Custom_Clip_Values,
               "eps": 16,
               "pixel_cnn": None,
               "batch_size": 32,
               "apply_fit": False,
               "apply_predict": True,
               "verbose":False,
            },
            SpatialSmoothing: {
              "window_size":  3,
              "channels_first":  False,
              "clip_values":Custom_Clip_Values,
              "apply_fit":False,
              "apply_predict": True,
            },
            ThermometerEncoding: {
                "clip_values": Custom_Clip_Values,
                "num_space":10,
                "channels_first":  False,
                "apply_fit":True,
                "apply_predict":True,
            },
            TotalVarMin: {
            "prob": 0.3,
            "norm":  2,
            "lamb": 0.5,
            "solver": "L-BFGS-B",
            "max_iter": 10,
            "clip_values":Custom_Clip_Values,
            "apply_fit": False,
            "apply_predict": True,
            "verbose": False,
            },
            VideoCompression: {
                "video_format": "avi" or "mp4" or "mkv",
                "constant_rate_factor": 28,
                "channels_first":False,
                "apply_fit":False,
                "apply_predict":  True,
                "verbose":  False,
            },
            Mixup: {
              "num_classes": Num_Classes,
              "alpha": 1.0,
              "num_mix": 2,
              "apply_fit": True,
              "apply_predict":False,
            },
            CutMix: {
                  "num_classes": Num_Classes,
                  "alpha":  1.0,
                  "probability":  0.5,
                  "channels_first":  False,
                  "apply_fit": True,
                  "apply_predict":  False,
                  "verbose":  False,
            },
            Cutout: {
                "length": Length,
                "channels_first":  False,
                "apply_fit": True,
                "apply_predict": False,
                "verbose": False,
            },
            GaussianAugmentation: {
                "sigma":  1.0,
                "augmentation":True,
                "ratio":  1.0,
                "clip_values": Custom_Clip_Values,
                "apply_fit":  True,
                "apply_predict": False,
            },
            ReverseSigmoid: {
                "beta":  1.0,
                "gamma":  0.1,
                "apply_fit":  False,
                "apply_predict": True,
            },
            Rounded: {
                "decimals":  3,
                "apply_fit":  False, 
                "apply_predict": True
            },
            Postprocessor: {
                "is_fitted":False, 
                "apply_fit":True, 
                "apply_predict": True
            },
            Resample: {
                "sr_original": Original_Sample_Rate,
                "sr_new": New_Sample_Rate,
                "channels_first":  False,
                "apply_fit": False,
                "apply_predict": True,
            },
            Preprocessor: {
                "is_fitted": False, 
                "apply_fit": True, 
                "apply_predict":  True
            },
            ClassLabels: {
                "apply_fit":False, 
                "apply_predict": True
            },
            ClusteringAnalyzer: {
                "num_clusters": 10,
            },
            GroundTruthEvaluator: {
                "metric": "accuracy",
            },
            ActivationDefence: {
                "activation_layer": "relu",
            },
            SubsetScanningDetector: {
                "subset_size": 100,
            },
            BinaryInputDetector: {
                "threshold": 0.5,
            },
            BinaryActivationDetector: {
                "activation_threshold": 0.5,
            },
            EvasionDetector: {
                "detector_type": "SVM",
            },
            PoisonFilteringDefence: {
                "filtering_rate": 0.1,
            },
            ProvenanceDefense: {
                "provenance_method": "hash",
            },
            RONIDefense: {
                "sensitivity": 0.1,
            },
            SpectralSignatureDefense: {
                "threshold": 0.1,
            },
            
        }

        defense_class = type(defense)
        params = default_params.get(defense_class, {})
        logger.info(f"Retrieved parameters for {defense_class.__name__}.")
        return params

    except Exception as e:
        logger.error(f"Error retrieving parameters for defense {type(defense).__name__}: {e}")
        raise

def get_all_defenses() -> List[DefenseType]:
    """
    Retrieve all defense classes available for testing.

    Returns:
        List[DefenseType]: List of defense classes.
    """
    try:
        defense_list = get_defense_list()
        logger.info(f"Retrieved {len(defense_list)} defense(s).")
        return defense_list
    except Exception as e:
        logger.error(f"Error retrieving all defenses: {e}")
        raise
print(get_all_defenses())