input: Write a function to create and train a machine learning model in Python using Keras that can distinguish between cats and dogs. The function should take the path to the dataset as an input and return the trained model.
  
  
  def train_cat_dog_classifier(dataset_path):
      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 = dataset_path, target_size = (64, 64), color_mode = 'rgb', class_mode = 'binary', batch_size = 32)
      model.fit_generator(generator = train_generator, steps_per_epoch = 25, epochs = 5)  # Adjust steps_per_epoch for your dataset size
  
      return model
  
  assert train_cat_dog_classifier('/path/to/dataset')!=None
  
output: def train_cat_dog_classifier(dataset_path):
      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
  
      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)
      model.fit_generator(generator = train_generator, steps_per_epoch = 25, epochs = 5)
      return model