 Here's a simplified example of how you might structure your code using the Paz library. Please note that this is a basic example and might need adjustments based on your specific requirements.

```python
import paz as pz
import numpy as np
import cv2
import random

# Define image and box augmentation pipelines
image_augmentation = pz.ProcessorA([
    pz.code_snippet(lambda img: cv2.GaussianBlur(img, (5, 5), 0)),  # Random contrast
    pz.code_snippet(lambda img: cv2.addWeighted(img, random.uniform(0.8, 1.2), np.zeros_like(img), 0, 0)),  # Random brightness
    pz.code_snippet(lambda img: cv2.addWeighted(img, random.uniform(0.8, 1.2), np.zeros_like(img), 0, random.uniform(-30, 30))),  # Random saturation
    pz.code_snippet(lambda img: cv2.addWeighted(img, 1, np.zeros_like(img), 0, random.uniform(-30, 30)))  # Random hue
])

box_augmentation = pz.ProcessorA([
    pz.ToImageBoxCoordinates(),
    pz.RandomFlipBoxesLeftRight(),
    pz.RandomSampleCrop(min_area=0.1),  # Replace with your desired box expansion method
])

# Define box preprocessing pipeline
box_preprocessing = pz.ProcessorA([
    pz.match_to_default_boxes(),  # Replace with your custom box matching function
    pz.encode_boxes(),
    pz.expand_class_label(),  # Replace with your custom one-hot encoding function
])

# Define a pipeline for drawing boxes
draw_boxes = pz.ProcessorA([
    pz.GetHeatmapsAndTags(),
    pz.TransformA(pz.point2D_a, pz.origin_A, pz.target_A)
])

# Create a sequential processor
processor = pz.SequentialProcessor([image_augmentation, box_augmentation, box_preprocessing, draw_boxes])

# Define a sequence generator
def sequence_generator(data):
    for i in range(0, len(data), 4):
        yield data[i:i+4]

# Demonstrate the image and box augmentations
image, boxes = ...  # Load an image and its boxes
augmented_image, augmented_boxes = processor(image, boxes)
cv2.imshow('Augmented Image', augmented_image)

# Put everything together in a single processor and demonstrate the sequence generator
full_processor = pz.SequentialProcessor([image_augmentation, box_augmentation, box_preprocessing, draw_boxes])
data = ...  # Load your dataset
for batch in sequence_generator(data):
    images, boxes = zip(*batch)
    augmented_images, augmented_boxes = full_processor(images, boxes)
    # Process the batch as needed
```

This code assumes that you have functions for loading images, loading boxes, matching boxes to default boxes, encoding boxes, expanding class labels, and loading your dataset. You'll also need to replace the placeholders with the appropriate functions from the Paz library or implement your own custom functions.