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 : 



import sys
from pathlib import Path
sys.path.append(str(Path(__name__).parents[0]))

from attacks import (
    AdversarialPatch,
    AdversarialPatchNumpy,
    AdversarialPatchTensorFlowV2,
    AdversarialPatchPyTorch,
    AdversarialTexturePyTorch,
    CarliniWagnerASR,
    AutoAttack,
    AutoProjectedGradientDescent,
    AutoConjugateGradient,
    BrendelBethgeAttack,
    BoundaryAttack,
    CompositeAdversarialAttackPyTorch,
    CarliniL2Method,
    CarliniLInfMethod,
    CarliniL0Method,
    DecisionTreeAttack,
    DeepFool,
    DPatch,
    RobustDPatch,
    ElasticNet,
    FastGradientMethod,
    FrameSaliencyAttack,
    FeatureAdversariesNumpy,
    FeatureAdversariesPyTorch,
    FeatureAdversariesTensorFlowV2,
    GeoDA,
    GRAPHITEBlackbox,
    GRAPHITEWhiteboxPyTorch,
    HighConfidenceLowUncertainty,
    HopSkipJump,
    ImperceptibleASR,
    ImperceptibleASRPyTorch,
    BasicIterativeMethod,
    LaserAttack,
    LowProFool,
    MomentumIterativeMethod,
    NewtonFool,
    OverloadPyTorch,
    MalwareGDTensorFlow,
    PixelAttack,
    ProjectedGradientDescent,
    ProjectedGradientDescentNumpy,
    ProjectedGradientDescentPyTorch,
    ProjectedGradientDescentTensorFlowV2,
    OverTheAirFlickeringPyTorch,
    SaliencyMapMethod,
    ShadowAttack,
    ShapeShifter,
    SimBA,
    SpatialTransformation,
    SquareAttack,
    ThresholdAttack,
    UniversalPerturbation,
    TargetedUniversalPerturbation,
    VirtualAdversarialMethod,
    Wasserstein,
    ZooAttack,
    SignOPTAttack
)



test all attack api of above using below code of reference (only pytorch frame work)

import sys
from pathlib import Path
sys.path.append(str(Path(__name__).parents[0]))



import sys
import logging
from pathlib import Path
from typing import Tuple

import torch
import torch.nn as nn
import torch.optim as optim
from art.estimators.classification import PyTorchClassifier
from attacks import AdversarialPatch


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


class SimpleCNN(nn.Module):
    """A simple convolutional neural network."""

    def __init__(self, num_classes: int) -> None:
        """Initialize the CNN architecture.

        Args:
            num_classes (int): Number of output classes.
        """
        super(SimpleCNN, self).__init__()
        self.conv1: nn.Conv2d = nn.Conv2d(
            in_channels=3, out_channels=32, kernel_size=3, stride=1, padding=1
        )
        self.conv2: nn.Conv2d = nn.Conv2d(
            in_channels=32, out_channels=64, kernel_size=3, stride=1, padding=1
        )
        self.adaptive_pool: nn.AdaptiveAvgPool2d = nn.AdaptiveAvgPool2d((32, 32))
        self.fc1: nn.Linear = nn.Linear(64 * 32 * 32, 128)
        self.fc2: nn.Linear = nn.Linear(128, num_classes)

    def forward(self, x: torch.Tensor) -> torch.Tensor:
        """Define the forward pass.

        Args:
            x (torch.Tensor): Input tensor.

        Returns:
            torch.Tensor: Output logits.
        """
        x = torch.relu(self.conv1(x))
        x = torch.relu(self.conv2(x))
        x = self.adaptive_pool(x)
        x = torch.flatten(x, 1)
        x = torch.relu(self.fc1(x))
        x = self.fc2(x)
        return x


def initialize_model(num_classes: int) -> nn.Module:
    """Initialize the CNN model.

    Args:
        num_classes (int): Number of output classes.

    Returns:
        nn.Module: Initialized model.
    """
    try:
        model = SimpleCNN(num_classes=num_classes)
        logger.info("Model initialized successfully.")
        return model
    except Exception as e:
        logger.error(f"Error initializing model: {e}")
        sys.exit(1)


def initialize_classifier(
    model: nn.Module,
    input_shape: Tuple[int, int, int],
    nb_classes: int,
    clip_values: Tuple[float, float],
    learning_rate: float = 0.001
) -> PyTorchClassifier:
    """Initialize the ART PyTorchClassifier.

    Args:
        model (nn.Module): PyTorch model.
        input_shape (Tuple[int, int, int]): Shape of input data.
        nb_classes (int): Number of classes.
        clip_values (Tuple[float, float]): Min and max values for input data.
        learning_rate (float, optional): Learning rate for optimizer. Defaults to 0.001.

    Returns:
        PyTorchClassifier: Wrapped classifier.
    """
    try:
        loss_fn = nn.CrossEntropyLoss()
        optimizer = optim.Adam(model.parameters(), lr=learning_rate)
        classifier = PyTorchClassifier(
            model=model,
            loss=loss_fn,
            optimizer=optimizer,
            input_shape=input_shape,
            nb_classes=nb_classes,
            clip_values=clip_values
        )
        logger.info("Classifier initialized successfully.")
        return classifier
    except Exception as e:
        logger.error(f"Error initializing classifier: {e}")
        sys.exit(1)


def generate_dummy_data(batch_size: int = 1) -> Tuple[torch.Tensor, torch.Tensor]:
    """Generate dummy input data and labels.

    Args:
        batch_size (int, optional): Number of samples. Defaults to 1.

    Returns:
        Tuple[torch.Tensor, torch.Tensor]: Inputs and labels.
    """
    try:
        # Generate random inputs
        inputs = torch.randn(batch_size, 3, 32, 32)
        # Generate random labels between 0 and 999
        labels = torch.randint(0, 1000, (batch_size,))
        logger.info("Dummy data generated successfully.")
        return inputs, labels
    except Exception as e:
        logger.error(f"Error generating dummy data: {e}")
        sys.exit(1)


def perform_adversarial_attack(
    classifier: PyTorchClassifier,
    inputs: torch.Tensor,
    labels: torch.Tensor
) -> None:
    """Perform adversarial patch attack on the inputs.

    Args:
        classifier (PyTorchClassifier): The classifier to attack.
        inputs (torch.Tensor): Input data.
        labels (torch.Tensor): True labels.
    """
    try:
        attack = AdversarialPatch(classifier=classifier, patch_shape=(3, 10, 10))
        adversarial_examples = attack.generate(x=inputs, y=labels)
        logger.info("Adversarial patch attack performed successfully.")
        logger.debug(f"Original Inputs: {inputs}")
        logger.debug(f"Adversarial Inputs: {adversarial_examples}")
    except Exception as e:
        logger.error(f"Error performing adversarial attack: {e}")
        sys.exit(1)


def main() -> None:
    """Main function to execute the end-to-end test."""
    logger.info("Starting end-to-end adversarial patch attack test.")

    # Initialize model
    num_classes = 1000
    model = initialize_model(num_classes=num_classes)

    # Initialize classifier
    input_shape = (3, 32, 32)
    clip_values = (0.0, 1.0)
    classifier = initialize_classifier(
        model=model,
        input_shape=input_shape,
        nb_classes=num_classes,
        clip_values=clip_values
    )

    # Generate dummy data
    inputs, labels = generate_dummy_data(batch_size=1)

    # Perform adversarial attack
    perform_adversarial_attack(classifier, inputs, labels)

    logger.info("End-to-end adversarial patch attack test completed successfully.")


if __name__ == "__main__":
    main()