input: Write a function that collates a machine learning model using Python's Keras library to distinguish between cats and dogs. The function should take a dataset path, a target image size, the number of training epochs, and the batch size as inputs and return a compiled Keras model. Use the provided neural network architecture and data generator settings. If the dataset path is invalid, the function should return None.
  
  def collate_cat_dog_model(dataset_path, target_size, epochs, batch_size):
      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
  
      try:
          # Create the neural network model
          model = Sequential()
  
          # Input layer
          model.add(Conv2D(32, (3, 3), input_shape = target_size + (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 = target_size, color_mode = 'rgb', class_mode = 'binary', batch_size = batch_size)
          model.fit_generator(generator = train_generator, steps_per_epoch = 800 // batch_size, epochs = epochs, validation_steps = 200 // batch_size)
          
          return model
      except Exception as e:
          return None
  
  assert collate_cat_dog_model('/path/to/dataset', (64, 64), 5, 32) is not None
  assert collate_cat_dog_model('/invalid/path', (64, 64), 5, 32) == None
output: def collate_cat_dog_model(dataset_path, target_size, epochs, batch_size):
      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
  
      try:
          # Create the neural network model
          model = Sequential()
  
          # Input layer
          model.add(Conv2D(32, (3, 3), input_shape = target_size + (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 = target_size, color_mode = 'rgb', class_mode = 'binary', batch_size = batch_size)
          model.fit_generator(generator = train_generator, steps_per_epoch = 800 // batch_size, epochs = epochs, validation_steps = 200 // batch_size)
          
          return model
      except Exception as e:
          return None