 ```python
import paz
import paz.image as image_ops
import paz.box as box_ops
import paz.data as data_ops
import paz.transforms as transforms
import paz.mtl as mtl_ops
import paz.vision as vision_ops
import tensorflow as tf

# Define image augmentation pipeline
image_pipe = paz.Pipeline(
    [
        image_ops.RandomContrast(),
        image_ops.RandomBrightness(),
        image_ops.RandomSaturation(),
        image_ops.RandomHue(),
    ]
)

# Define box augmentation pipeline
box_pipe = paz.Pipeline(
    [
        box_ops.ToImageBoxCoordinates(),
        box_ops.Expand(),
        box_ops.RandomSampleCrop(),
        box_ops.RandomFlipLeftRight(),
    ]
)

# Define box drawing pipeline
draw_box_pipe = paz.Pipeline(
    [
        box_ops.DrawBoxes(),
    ]
)

# Define box preprocessing pipeline
preprocess_box_pipe = paz.Pipeline(
    [
        box_ops.MatchDefaultBoxes(),
        box_ops.Encode(),
        mtl_ops.ExpandClassLabel(),
    ]
)

# Define processor
processor = paz.Processor(
    image_pipe=image_pipe,
    box_pipe=box_pipe,
    draw_box_pipe=draw_box_pipe,
    preprocess_box_pipe=preprocess_box_pipe,
)

# Define sequence generator
def sequence_generator():
    image, boxes = next(image_dataset)
    image = processor.preprocess_image(image)
    boxes = processor.preprocess_boxes(boxes)
    return image, boxes

image_dataset = tf.data.Dataset.from_tensor_slices((image_tensor, box_tensor))
image_dataset = image_dataset.map(sequence_generator).batch(batch_size)

# Demonstrate image and box augmentations
for image, boxes in image_dataset:
    vision_ops.draw_boxes(image, boxes)
    tf.keras.preprocessing.image.save_img('output.jpg', image)
```