input: Write a function to collate a machine learning model in Python that distinguishes between cats and dogs. The function should accept a dataset of images and return the compiled model. It should also fit the model on the 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_and_fit_model(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)
      test_generator = datagen.flow_from_directory(directory='/path/to/test_dataset', 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=test_generator, validation_steps=200)
      
      return model
  
  # Test cases
  model = create_and_fit_model('/path/to/dataset')
  
output: def create_and_fit_model(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)
      test_generator = datagen.flow_from_directory(directory='/path/to/test_dataset', 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=test_generator, validation_steps=200)
      return model