How to do I apply the same augmentations to two sets of similar data - tensorflow-datasets

Question

I am trying to train an image translation model with TensorFlow, the dataset consists of two equal length list of images, how do I apply a map both of them so they receive the same augmentations.
I have tried to create a variable to keep track of which iteration it is, as well as using a seeded random number.
def load_and_preprocess_color(path):
image = tf.read_file(path)
image = tf.image.decode_png(image, channels=3)
image = tf.image.resize(image, [224, 224])
image = tf.image.random_flip_left_right(image)
image /= 255.0 # normalize to [0,1] range
return image
def load_and_preprocess_gray(path):
image = tf.read_file(path)
image = tf.image.decode_png(image, channels=1)
image = tf.image.resize(image, [224, 224])
image = tf.image.random_flip_left_right(image)
image /= 255.0 # normalize to [0,1] range
return image
input_data_root = pathlib.Path('Images/Gray/') output_data_root =
pathlib.Path('Images/Color/')
input_image_paths = [str(item) for item in input_data_root.iterdir()]
output_image_paths = [str(item) for item in
output_data_root.iterdir()] image_count = len(input_image_paths)
input_path_ds = tf.data.Dataset.from_tensor_slices(input_image_paths)
output_path_ds =
tf.data.Dataset.from_tensor_slices(output_image_paths)
input_ds =
input_path_ds.map(load_and_preprocess_gray,num_parallel_calls=AUTOTUNE)
output_ds =
output_path_ds.map(load_and_preprocess_color,num_parallel_calls=AUTOTUNE`
I expect two pieces of data at the same position to be either flipped of not flipped, but its flipped state to be the same as the other