TypeError: len() of unsized object in pyclustering library

I am using the pyclustering library to perform K-means. The datasets I am using are being read in CSV format as shown in the code below. I have tried passing X_scaled as a numpy array, as a list using to_list(). However, I constantly get this error:

TypeError: len() of unsized object

Version of pyclustering: 0.10.1.2

The code is below:

from pyclustering.cluster.kmeans import kmeans
from pyclustering.utils.metric import distance_metric, type_metric
import matplotlib.pyplot as plt
import numpy as np

# Define a function to convert distance metric names to functions
def get_distance_metric(metric_name):
    if metric_name == 'euclidean':
        return distance_metric(type_metric.EUCLIDEAN)
    elif metric_name == 'squared euclidean':
        return distance_metric(type_metric.EUCLIDEAN_SQUARE)
    elif metric_name == 'manhattan':
        return distance_metric(type_metric.MANHATTAN)
    elif metric_name == 'chebyshev':
        return distance_metric(type_metric.CHEBYSHEV)
    elif metric_name == 'canberra':
        return distance_metric(type_metric.CANBERRA)
    elif metric_name == 'chi-square':
        return distance_metric(type_metric.CHI_SQUARE)
    else:
        raise ValueError(f"Unsupported distance metric: {metric_name}")

# Define the distance measures dictionary
distance_measures = {'euclidean': 0, 'squared euclidean': 1, 'manhattan': 2, 'chebyshev': 3, 
                    'canberra': 5, 'chi-square': 6}

# Example of running the modified code
datasets = main_datasets
df = datasets['circles0.3.csv']

original_labels = df['label'].values if 'label' in df.columns else None
X = df.drop(columns=['label'], errors='ignore').values
scaler = StandardScaler()
X_scaled = scaler.fit_transform(X)
    
# Set the number of clusters
k = 3

# Experiment with various distance metrics
for metric_name, metric_code in distance_measures.items():
    # Get the distance metric function
    distance_metric_func = get_distance_metric(metric_name)
    
    # Perform K-means clustering with the selected distance metric
    
    # centers, clusters = kmeans(X_scaled.tolist(), k, metric=distance_metric_func)
    centers, clusters = kmeans(X_scaled, k, metric=distance_metric_func)
    
    # Plot the clusters
    plt.figure()
    plt.title(f'K-means Clustering with {metric_name}')
    plt.xlabel('X')
    plt.ylabel('Y')
    plt.scatter([point[0] for point in X_scaled], [point[1] for point in X_scaled], c=clusters, cmap='viridis')
    plt.scatter([center[0] for center in centers], [center[1] for center in centers], marker='x', c='red', s=100)
    plt.show()

Can anybody help me out with what the issue might be with this code?

In the following function call, you should add 'initial centers' argument instead of k. Additionally, you must convert X_scaled array to a list before passing to kmeans function.

centers, clusters = kmeans(X_scaled, k, metric=distance_metric_func)

Use the below code instead:

from pyclustering.cluster.center_initializer import random_center_initializer    
X_scaled_list = X_scaled.tolist()
initial_centers = random_center_initializer(X_scaled_list,2).initialize()
result_1 = kmeans(X_scaled_list, initial_centers)