fix IRV transformer

      n_samples * np.array of size (2*K,)

      each array includes K similarity values and corresponding labels

    """

    import tensorflow as tf

    features = []

    similarity_xs = similarity * np.sign(w)

    [target_len, reference_len] = similarity_xs.shape

                                                 100, target_len), :]

      # generating batch of data by slicing similarity matrix

      # into 100*reference_dataset_length

      value, indice = tf.nn.top_k(similarity, k=self.K + 1, sorted=True)

      top_label = tf.gather(y, indice)

      indice = np.argsort(similarity)[:, -(self.K + 1):][:, ::-1]

      value = np.take_along_axis(similarity, indice, axis=1)

      top_label = np.take(y, indice)

      values.append(value)

      top_labels.append(top_label)

    values_np = np.concatenate(values, axis=0)