Edit: Although after bounty period, but here is an example hacky code that does that.

This is a neural network trained to create outputs that are equally distant from each other.

import numpy as np
from sklearn import neural_network

def get_new_targets(x):
    """
    Function to get points that are in the middle of the neighbouring points"""
    try:
        x = [e[1] for e in x]
    except:
        pass
    enumerated_x = [e for e in enumerate(x)]
    enumerated_x.sort(key=lambda x: x[1])
    targets = []
    for i, elem in enumerate(enumerated_x):
        previous_sample, next_sample = 0, 0
#        current_sample =  enumerated_x[i][1]
        if i == 0:
            previous_sample = 0
        else:
            previous_sample = enumerated_x[i-1][1]
        if i == len(enumerated_x)-1:
            next_sample = 50
        else:            
            next_sample = enumerated_x[i+1][1]            
        target = (previous_sample + next_sample)/2. 
        targets.append(target)        
    targets_in_order = sorted([(enum[0], t) for enum, t in zip(enumerated_x, targets)])
    targets_in_order = [e[1] for e in targets_in_order]
    return np.array(targets_in_order)



# create training data
# in this example we care only about one axis
X = np.array([[0,3], [0,4], [0,10], [0,15], [0,35], [0,36] ,[0,50]])
# create initial targers. This will change every epoch
Y = get_new_targets(X)

preds = [] # save predictions
epochs = []

# warm start True and max_iter 1 to simulate batch training
clf = neural_network.MLPRegressor(max_iter=1, warm_start=True, alpha=0.005, 
                                  hidden_layer_sizes=(50,), activation='logistic')

for i in range(4000): # for each epoch
    # since warms_start=True it will reuse weights from previous epoch
    clf.fit(X, Y) 
    predictions =  clf.predict(X) 
    Y = get_new_targets(predictions)
    if i % 100 == 0:
        pred = clf.predict(X)        
        preds.append([pred])
        epochs.append(i)       

Plots that show that it kindof worked, but not really