input: Write a function to create a machine learning model in Python using Keras that can distinguish between cats and dogs from a given dataset of 800 images. The function should define the neural network architecture, compile the model, and train it using an ImageDataGenerator. If the model successfully trains and distinguishes between cats and dogs, return True, otherwise return False. If the dataset path is incorrect or there are any errors during the training, return False.
  assert train_cat_dog_model("/path/to/dataset")==True
  assert train_cat_dog_model("/invalid/path")==False
output: def train_cat_dog_model(dataset_path):
      try:
          from tensorflow.keras.models import Sequential
          from tensorflow.keras.layers import Conv2D, MaxPooling2D, Flatten, Dense
          from tensorflow.keras.preprocessing.image import ImageDataGenerator
          import os
  
          if not os.path.exists(dataset_path):
              return False
  
          model = Sequential([
              Conv2D(32, (3, 3), activation='relu', input_shape=(150, 150, 3)),
              MaxPooling2D(2, 2),
              Conv2D(64, (3, 3), activation='relu'),
              MaxPooling2D(2, 2),
              Conv2D(128, (3, 3), activation='relu'),
              MaxPooling2D(2, 2),
              Flatten(),
              Dense(512, activation='relu'),
              Dense(1, activation='sigmoid')
          ])
  
          model.compile(optimizer='adam', loss='binary_crossentropy', metrics=['accuracy'])
  
          train_datagen = ImageDataGenerator(rescale=1./255)
          train_generator = train_datagen.flow_from_directory(
              dataset_path,
              target_size=(150, 150),
              batch_size=20,
              class_mode='binary'
          )
  
          model.fit(train_generator, steps_per_epoch=40, epochs=10)
  
          return True
      except:
          return False