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 test below api with dummy data like below , end to end 



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
)

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

class SimpleCNN(nn.Module):
    def __init__(self, num_classes):
        super(SimpleCNN, self).__init__()
        self.conv1 = nn.Conv2d(in_channels=3, out_channels=32, kernel_size=3, stride=1, padding=1)
        self.conv2 = nn.Conv2d(in_channels=32, out_channels=64, kernel_size=3, stride=1, padding=1)
        # Adaptive pooling to ensure output size is (32, 32)
        self.adaptive_pool = nn.AdaptiveAvgPool2d((32, 32))
        self.fc1 = nn.Linear(64 * 32 * 32, 128)
        self.fc2 = nn.Linear(128, num_classes)

    def forward(self, x):
        x = torch.relu(self.conv1(x))
        x = torch.relu(self.conv2(x))
        # Apply adaptive pooling here
        x = self.adaptive_pool(x)
        x = torch.flatten(x, 1)
        x = torch.relu(self.fc1(x))
        x = self.fc2(x)
        return x

# Model, loss function, and optimizer
model = SimpleCNN(num_classes=1000)
loss_fn = nn.CrossEntropyLoss()
optimizer = optim.Adam(model.parameters(), lr=0.001)
# Updated input size
input = torch.randn(3, 32, 32)

# Define the classifier with correct input shape
classifier = PyTorchClassifier(
    model=model,
    loss=loss_fn,
    optimizer=optimizer,
    input_shape=(3, 32, 32),  
    nb_classes=1000,  
    clip_values=(0, 1)
)

# Generate adversarial patch
attack = AdversarialPatch(classifier=classifier, patch_shape=(3, 10, 10))