input: def classify_cats_and_dogs(dataset_path: str) -> None:
      """ Create a machine learning model in Python that distinguishes between cats and dogs using the given dataset.
      The output is the trained model ready for prediction.
  
      Example:
      classify_cats_and_dogs('/path/to/dataset')
      # This will train the model using the /path/to/dataset and output a trained model.
      """
output:     
      import os
      import cv2
      import numpy as np
      from sklearn.model_selection import train_test_split
      from sklearn.preprocessing import LabelEncoder
      from tensorflow.keras.models import Sequential
      from tensorflow.keras.layers import Conv2D, MaxPooling2D, Flatten, Dense
      from tensorflow.keras.utils import to_categorical
  
      # Load dataset
      def load_images_and_labels(dataset_path):
          images = []
          labels = []
          for label in os.listdir(dataset_path):
              label_path = os.path.join(dataset_path, label)
              if os.path.isdir(label_path):
                  for img_name in os.listdir(label_path):
                      img_path = os.path.join(label_path, img_name)
                      img = cv2.imread(img_path, cv2.IMREAD_GRAYSCALE)
                      img = cv2.resize(img, (64, 64))
                      images.append(img)
                      labels.append(label)
          return np.array(images), np.array(labels)
  
      images, labels = load_images_and_labels(dataset_path)
  
      # Preprocess data
      images = images.reshape(images.shape[0], 64, 64, 1).astype('float32') / 255
      label_encoder = LabelEncoder()
      labels = label_encoder.fit_transform(labels)
      labels = to_categorical(labels)
  
      # Split dataset
      X_train, X_test, y_train, y_test = train_test_split(images, labels, test_size=0.2, random_state=42)
  
      # Build model
      model = Sequential([
          Conv2D(32, (3, 3), activation='relu', input_shape=(64, 64, 1)),
          MaxPooling2D((2, 2)),
          Conv2D(64, (3, 3), activation='relu'),
          MaxPooling2D((2, 2)),
          Flatten(),
          Dense(128, activation='relu'),
          Dense(2, activation='softmax')
      ])
  
      model.compile(optimizer='adam', loss='categorical_crossentropy', metrics=['accuracy'])
  
      # Train model
      model.fit(X_train, y_train, epochs=10, validation_data=(X_test, y_test))
  
      # Save model
      model.save('cats_dogs_model.h5')
  