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 :Image classification metrics pipeline  we develop from scratch , prediction or logits or  labels  

example :(keep this in the mind ) 
for batch in dataloader:
    images, targets = batch

one example type 1
 outputs = model(**batch)
 loss = outputs.loss

second example type 2


data, target = data.to(device), target.to(device)
            output = model(data)


Using only `torch module`  for develop Generalised and robustness , scalable, Reusable 
NOTE: use only torch module 

Accuracy: Overall percentage of correct predictions.
Top-k Accuracy: Accuracy considering the top-k predictions (e.g., Top-5 accuracy).
Precision: Ratio of true positive predictions to all positive predictions.
Recall (Sensitivity): Ratio of true positives to actual positives in the data.
F1 Score: Harmonic mean of precision and recall.
Area Under the ROC Curve (AUC-ROC): Measures model performance across various thresholds.
Area Under the Precision-Recall Curve (AUC-PR): Measures model performance on imbalanced datasets.
Log Loss (Cross-Entropy Loss): Evaluates the confidence of predictions with the true labels.
Mean Absolute Error (MAE): Average absolute difference between predicted probabilities and actual labels.
Mean Squared Error (MSE): Average squared difference between predicted and actual labels.
Confusion Matrix: Matrix displaying true positives, true negatives, false positives, and false negatives.
Balanced Accuracy: Average of recall obtained in each class, useful for imbalanced datasets.
Matthews Correlation Coefficient (MCC): Measures correlation between predicted and actual labels; robust for imbalanced datasets.
Kappa Statistic (Cohen’s Kappa): Measures agreement between predictions and actual labels, correcting for chance.
Brier Score: Measures the accuracy of probabilistic predictions, sensitive to calibration.
Expected Calibration Error (ECE): Measures the difference between predicted probabilities and actual accuracy.
Classification Calibration Error (CCE): A recent measure (post-2023) evaluating how well the probability outputs are calibrated for each class.
Uncertainty Calibration Error (UCE): Post-2023 measure for uncertainty calibration in models, especially in high-dimensional classes.
Jaccard Index (Intersection over Union): Measures similarity between prediction and ground truth.
True Skill Statistic (TSS): Useful for evaluating models on unbalanced data, balancing sensitivity and specificity.
Hinge Loss: A metric used primarily with SVMs, but applied here for multiclass classifications.
Average Precision (AP): Precision averaged across recall levels, especially useful in object-centric classification tasks.
False Positive Rate (FPR): Rate of incorrect positive predictions among all negative samples.
False Negative Rate (FNR): Rate of missed positive predictions among all actual positive samples.
Negative Log Likelihood (NLL): Evaluates model’s confidence in the correct class, with lower values indicating better performance.
Focal Loss: A metric that down-weights easy examples and focuses more on hard-to-classify examples, widely used for imbalanced datasets.
Out-of-Distribution (OoD) Detection Accuracy: Evaluates the model’s ability to identify samples outside the training data classes (post-2023).
Entropy: Measures the uncertainty of model predictions, with higher entropy indicating less confident predictions.
Lift (Gains): Assesses model improvement over random guessing, especially in classifying rare classes.
False Discovery Rate (FDR): Fraction of false positives among all positive predictions, useful for imbalanced classes.