moonlight/glyphs/knn.py [68:78]:
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    distance_matrix = _squared_euclidean_distance_matrix(
        flattened_patches, flattened_centroids)

    # Take the k centroids with the lowest distance to each patch. Wrap the k
    # constant in a tf.identity, which tests can use to feed in another value.
    k_value = tf.identity(tf.constant(K_NEAREST_VALUE), name='k_nearest_value')
    nearest_centroid_inds = tf.nn.top_k(-distance_matrix, k=k_value)[1]
    # Get the label corresponding to each nearby centroids, and reshape the
    # labels back to the original shape.
    nearest_labels = tf.reshape(
        tf.gather(labels, tf.reshape(nearest_centroid_inds, [-1])),
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moonlight/glyphs/knn_model.py [67:77]:
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      distance_matrix = _squared_euclidean_distance_matrix(
          flattened_patches, flattened_centroids)

    # Take the k centroids with the lowest distance to each patch. Wrap the k
    # constant in a tf.identity, which tests can use to feed in another value.
    k_value = tf.identity(tf.constant(K_NEAREST_VALUE), name='k_nearest_value')
    nearest_centroid_inds = tf.nn.top_k(-distance_matrix, k=k_value)[1]
    # Get the label corresponding to each nearby centroids, and reshape the
    # labels back to the original shape.
    nearest_labels = tf.reshape(
        tf.gather(labels, tf.reshape(nearest_centroid_inds, [-1])),
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