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Given the following text problem statement, write Python code to implement the functionality described below in problem statement
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Problem:
how you can incorporate a preprocessing layer into a classification network and train it using a dataset

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Python Code::
from tensorflow.keras.utils import image_dataset_from_directory
import tensorflow as tf
import matplotlib.pyplot as plt
PATH='.../Citrus/Leaves'  # modify to your path
ds = image_dataset_from_directory(PATH,
                                  validation_split=0.2, subset="training",
                                  image_size=(256,256), interpolation="mitchellcubic",
                                  crop_to_aspect_ratio=True,
                                  seed=42, shuffle=True, batch_size=32)
AUTOTUNE = tf.data.AUTOTUNE
ds = ds.cache().prefetch(buffer_size=AUTOTUNE)
num_classes = 5
model = tf.keras.Sequential([
  tf.keras.layers.RandomFlip("horizontal_and_vertical"),
  tf.keras.layers.RandomRotation(0.2),
  tf.keras.layers.Rescaling(1/127.0, offset=-1),
  tf.keras.layers.Conv2D(32, 3, activation='relu'),
  tf.keras.layers.MaxPooling2D(),
  tf.keras.layers.Conv2D(32, 3, activation='relu'),
  tf.keras.layers.MaxPooling2D(),
  tf.keras.layers.Conv2D(32, 3, activation='relu'),
  tf.keras.layers.MaxPooling2D(),
  tf.keras.layers.Flatten(),
  tf.keras.layers.Dense(128, activation='relu'),
  tf.keras.layers.Dense(num_classes)
])
model.compile(optimizer='adam',
              loss=tf.keras.losses.SparseCategoricalCrossentropy(from_logits=True),
              metrics=['accuracy'])
model.fit(ds, epochs=3)

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