diff --git a/src/graphnet/models/graphs/utils.py b/src/graphnet/models/graphs/utils.py
index 9587010df..0ac8aeac2 100644
--- a/src/graphnet/models/graphs/utils.py
+++ b/src/graphnet/models/graphs/utils.py
@@ -113,72 +113,6 @@ def identify_indices(
     return cluster_indices, summarization_indices, features_for_summarization
 
 
-# TODO Remove this function as it is superseded by
-# cluster_and_pad wich has the same functionality
-def cluster_summarize_with_percentiles(
-    x: np.ndarray,
-    summarization_indices: List[int],
-    cluster_indices: List[int],
-    percentiles: List[int],
-    add_counts: bool,
-) -> np.ndarray:
-    """Turn `x` into clusters with percentile summary.
-
-    From variables specified by column indices `cluster_indices`, `x` is turned
-    into clusters. Information in columns of `x` specified by indices
-    `summarization_indices` with each cluster is summarized using percentiles.
-    It is assumed `x` represents a single event.
-
-    **Example use-case**:
-    Suppose `x` contains raw pulses from a neutrino event where some DOMs have
-    multiple measurements of Cherenkov radiation. If `cluster_indices` is set
-    to the columns corresponding to the xyz-position of the DOMs, and the
-    features specified in `summarization_indices` correspond to time, charge,
-    then each row in the returned array will correspond to a DOM,
-    and the time and charge for each DOM will be summarized by percentiles.
-    Returned output array has dimensions
-    `[n_clusters,
-    len(percentiles)*len(summarization_indices) + len(cluster_indices)]`
-
-    Args:
-        x: Array to be clustered
-        summarization_indices: List of column indices that defines features
-                                that will be summarized with percentiles.
-        cluster_indices: List of column indices on which the clusters
-                        are constructed.
-        percentiles: percentiles used to summarize `x`. E.g. [10,50,90].
-
-    Returns:
-        Percentile-summarized array
-    """
-    print(
-        "This function is deprecated and will be removed,",
-        "use the class cluster_and_pad with add_percentile_summary",
-        "instead for the same functionality",
-    )
-    pct_dict = {}
-    for feature_idx in summarization_indices:
-        summarized_array, column_offset, counts = gather_cluster_sequence(
-            x, feature_idx, cluster_indices
-        )
-        pct_dict[feature_idx] = np.nanpercentile(
-            summarized_array[:, column_offset:], percentiles, axis=1
-        ).T
-
-    for i, key in enumerate(pct_dict.keys()):
-        if i == 0:
-            array = summarized_array[:, 0:column_offset]
-
-        array = np.concatenate([array, pct_dict[key]], axis=1)
-
-    if add_counts:
-        array = np.concatenate(
-            [array, np.log10(counts).reshape(-1, 1)], axis=1
-        )
-
-    return array
-
-
 class cluster_and_pad:
     """Cluster and pad the data for further summarization.