Oh I thought they were same:)
Yes now it prints out FloatTensor. I try to get a random subset from the train_dataset with the following code,
    for class_type in labels.unique():
        indices = np.where(labels == class_type)
        sample_indices = torch.randint(int(indices[0][0]),
                                       int(indices[0][-1]),
                                       (samples_per_class,)).long()

        real_sample_pos = torch.cat((real_sample_pos, real_indices[sample_indices]))


    if shuffle:
        np.random.permutation(real_sample_pos)

    self.small_train_set = self.train_dataset.train_data[real_sample_pos].type(torch.FloatTensor)
    self.small_train_labels = self.train_dataset.train_labels[real_sample_pos]

Is there a more elegant way where I can avoid .type(torch.FloatTensor) ?
Thank you very much for your help!