input: def distinguish_cats_from_dogs(dataset_path: str) -> None:
      """
      Collate a machine learning model in Python that distinguishes between cats and dogs using a given dataset of images.
      
      >>> distinguish_cats_from_dogs('/path/to/dataset')
      """
output:     
      import os
      import numpy as np
      from tensorflow.keras.preprocessing.image import ImageDataGenerator
      from tensorflow.keras.models import Sequential
      from tensorflow.keras.layers import Conv2D, MaxPooling2D, Flatten, Dense
      from tensorflow.keras.optimizers import Adam
  
      # Define constants
      IMG_WIDTH, IMG_HEIGHT = 150, 150
      BATCH_SIZE = 32
      EPOCHS = 10
  
      # Load dataset
      train_dir = os.path.join(dataset_path, 'train')
      validation_dir = os.path.join(dataset_path, 'validation')
  
      # Data augmentation and normalization
      train_datagen = ImageDataGenerator(rescale=1./255,
                                         rotation_range=40,
                                         width_shift_range=0.2,
                                         height_shift_range=0.2,
                                         shear_range=0.2,
                                         zoom_range=0.2,
                                         horizontal_flip=True,
                                         fill_mode='nearest')
  
      validation_datagen = ImageDataGenerator(rescale=1./255)
  
      train_generator = train_datagen.flow_from_directory(train_dir,
                                                          target_size=(IMG_WIDTH, IMG_HEIGHT),
                                                          batch_size=BATCH_SIZE,
                                                          class_mode='binary')
  
      validation_generator = validation_datagen.flow_from_directory(validation_dir,
                                                                    target_size=(IMG_WIDTH, IMG_HEIGHT),
                                                                    batch_size=BATCH_SIZE,
                                                                    class_mode='binary')
  
      # Build the model
      model = Sequential([
          Conv2D(32, (3, 3), activation='relu', input_shape=(IMG_WIDTH, IMG_HEIGHT, 3)),
          MaxPooling2D(2, 2),
          Conv2D(64, (3, 3), activation='relu'),
          MaxPooling2D(2, 2),
          Conv2D(128, (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')
      ])
  
      # Compile the model
      model.compile(optimizer=Adam(learning_rate=1e-4),
                    loss='binary_crossentropy',
                    metrics=['accuracy'])
  
      # Train the model
      model.fit(train_generator,
                steps_per_epoch=train_generator.samples // BATCH_SIZE,
                epochs=EPOCHS,
                validation_data=validation_generator,
                validation_steps=validation_generator.samples // BATCH_SIZE)
  
      # Save the model
      model.save('cats_vs_dogs_model.h5')
  