input: 
  def distinguish_cats_dogs(dataset_path: str, image_size: tuple, input_shape: tuple) -> keras.models.Sequential:
      """
      Create a machine learning model using Keras to distinguish between cats and dogs.
      The model should include convolutional layers, max pooling layers, dropout layers,
      and dense layers. The model should be compiled with binary cross-entropy loss and
      the Adam optimizer. The function should also return the trained model.
      Example:
      for distinguish_cats_dogs('/path/to/dataset', (64, 64), (64, 64, 3))
      Output: A trained Keras model
      """
      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
  
      # Create the neural network model
      model = Sequential()
  
      # Input layer
      model.add(Conv2D(32, (3, 3), input_shape=input_shape, 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=image_size, color_mode='rgb', class_mode='binary', batch_size=32)
      model.fit_generator(generator=train_generator, steps_per_epoch=800, epochs=5, validation_data=test_generator, validation_steps=200)
      
      return model
  
output:     
      # Create test data generator
      test_generator = datagen.flow_from_directory(directory=dataset_path, target_size=image_size, color_mode='rgb', class_mode='binary', batch_size=32)
  
      # Return the trained model
      return model
  