input: 
  def identify_cats_and_dogs(train_dir: str, epochs: int, batch_size: int) -> keras.models.Sequential:
      """
      Train a machine learning model using Keras to distinguish between cats and dogs.
      The model is built with Conv2D, MaxPooling2D, Dropout, and Dense layers.
  
      >>> identified_model = identify_cats_and_dogs('/path/to/dataset', 5, 32)
      >>> type(identified_model)
      <class 'keras.models.Sequential'>
      """
      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=(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=train_dir, target_size=(64, 64), color_mode='rgb', class_mode='binary', batch_size=batch_size)
      steps_per_epoch = int(np.ceil(800 / batch_size))
      validation_data = datagen.flow_from_directory(directory='/path/to/validation', target_size=(64, 64), color_mode='rgb', class_mode='binary', batch_size=batch_size)
      validation_steps = int(np.ceil(200 / batch_size))
      model.fit(train_generator, steps_per_epoch=steps_per_epoch, epochs=epochs, validation_data=validation_data, validation_steps=validation_steps)
  
      return model
  
output:     
      # Save the model
      model.save('cats_and_dogs_model.h5')
  
      return model
  