input: Write a function to create a machine learning model in Python that distinguishes between cats and dogs using a dataset of 800 images. The function should include data preprocessing with ImageDataGenerator and train the model using the given dataset.
  
  import numpy as np
  import keras
  from keras.layers import Dense, Conv2D, MaxPooling2D, Dropout, Flatten
  from keras.models import Sequential
  from keras.preprocessing.image import ImageDataGenerator
  
  def create_cat_dog_classifier(dataset_path):
      # Create the neural network model
      model = Sequential()
  
      # Input layer
      model.add(Conv2D(32, (3, 3), input_shape=(64, 64, 3), activation='relu'))
  
      # Hidden layers
      model.add(MaxPooling2D(pool_size=(2, 2)))
      model.add(Dropout(0.3))
      model.add(Conv2D(64, (3, 3), activation='relu'))
      model.add(MaxPooling2D(pool_size=(2, 2)))
      model.add(Dropout(0.3))
      model.add(Conv2D(64, (3, 3), activation='relu'))
      model.add(MaxPooling2D(pool_size=(2, 2)))
      model.add(Dropout(0.3))
  
      # Output layer
      model.add(Flatten())
      model.add(Dense(units=128, activation='relu'))
      model.add(Dense(units=1, activation='sigmoid'))
  
      # Compile the model
      model.compile(loss='binary_crossentropy', optimizer='adam', metrics=['accuracy'])
  
      # Create data generator
      datagen = ImageDataGenerator(rescale=1./255, shear_range=0.2, zoom_range=0.2, horizontal_flip=True)
  
      # Fit the model
      train_generator = datagen.flow_from_directory(directory=dataset_path, target_size=(64, 64), color_mode='rgb', class_mode='binary', batch_size=32)
      model.fit_generator(generator=train_generator, steps_per_epoch=800, epochs=5, validation_data=train_generator, validation_steps=200)
  
      return model
output: def create_cat_dog_classifier(dataset_path):
      model = Sequential()
      model.add(Conv2D(32, (3, 3), input_shape=(64, 64, 3), activation='relu'))
      model.add(MaxPooling2D(pool_size=(2, 2)))
      model.add(Dropout(0.3))
      model.add(Conv2D(64, (3, 3), activation='relu'))
      model.add(MaxPooling2D(pool_size=(2, 2)))
      model.add(Dropout(0.3))
      model.add(Conv2D(64, (3, 3), activation='relu'))
      model.add(MaxPooling2D(pool_size=(2, 2)))
      model.add(Dropout(0.3))
      model.add(Flatten())
      model.add(Dense(units=128, activation='relu'))
      model.add(Dense(units=1, activation='sigmoid'))
      model.compile(loss='binary_crossentropy', optimizer='adam', metrics=['accuracy'])
      datagen = ImageDataGenerator(rescale=1./255, shear_range=0.2, zoom_range=0.2, horizontal_flip=True)
      train_generator = datagen.flow_from_directory(directory=dataset_path, target_size=(64, 64), color_mode='rgb', class_mode='binary', batch_size=32)
      model.fit_generator(generator=train_generator, steps_per_epoch=800, epochs=5, validation_data=train_generator, validation_steps=200)
      return model