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# Training loop for epoch in range(100): # loop over the dataset multiple times for i, data in enumerate(dataset): # forward pass optimizer.zero_grad() outputs = model(data) loss = loss_fn(outputs, targets) loss.backward() optimizer.step()

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def optimize_image(image_path, output_path, size=(1280, 720)): try: img = Image.open(image_path) img.thumbnail(size) # Resizes the image img.save(output_path, optimize=True, quality=90) # Saves with optimized compression print(f"Image optimized and saved to: output_path") except Exception as e: print(f"An error occurred: e")

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import cv2 import numpy as np