Sample a training batch (data and label).

def sample_train_batch(self):
        """Sample a training batch (data and label)."""
        batch = []
        labels = []
        num_groups = self.batch_size // self.batch_k

        # For CUB200, we use the first 100 classes for training.
        sampled_classes = np.random.choice(100, num_groups, replace=False)
        for i in range(num_groups):
            img_fnames = np.random.choice(self.train_image_files[sampled_classes[i]],
                                          self.batch_k, replace=False)
            batch += [self.get_image(img_fname, is_train=True) for img_fname in img_fnames]
            labels += [sampled_classes[i] for _ in range(self.batch_k)]

        return nd.concatenate(batch, axis=0), labels