Why does PyGad fitness_function not work when inside of a class?

I am trying to train a genetic algorithm but for some reason it does not work when it's stored inside of a class. I have two equivalent pieces of code but the one stored inside of a class fails. It returns this..

raise ValueError("The fitness function must accept 2 parameters:
1) A solution to calculate its fitness value.
2) The solution's index within the population.

The passed fitness function named '{funcname}' accepts {argcount} parameter(s).".format(funcname=fitness_func.__code__.co_name, argcount=fitness_func.__code__.co_argcount))
ValueError: The fitness function must accept 2 parameters:
1) A solution to calculate its fitness value.
2) The solution's index within the population.

The passed fitness function named 'fitness_func' accepts 3 parameter(s).

Here is the simplified version of the one that doesnt work.

import torch
import torch.nn as nn
import pygad.torchga
import pygad

class NN(nn.Module):
    def __init__(self, input_size, hidden_size, output_size):
        super().__init__()
        self.linear1 = nn.Linear(input_size, hidden_size)
        self.linear2 = nn.Linear(hidden_size, hidden_size)
        self.linear3 = nn.Linear(hidden_size, hidden_size)
        self.linear4 = nn.Linear(hidden_size, output_size)

    def forward(self, x):
        x = self.linear1(x)
        x = self.linear2(x)
        x = self.linear3(x)
        x = self.linear4(x)
        return x

class Coin:
    def __init__(self):
        self.NeuralNet = NN(1440, 1440, 3)

    def fitness_func(self, solution, solution_idx):
        return 0

    def trainModel(self):

        torch_ga = pygad.torchga.TorchGA(model=self.NeuralNet, num_solutions=10)

        ga_instance = pygad.GA(num_generations=10,
                               num_parents_mating=2,
                               initial_population=torch_ga.population_weights,
                               fitness_func=self.fitness_func)

        ga_instance.run()

if __name__ == "__main__":
    coin = Coin()
    coin.trainModel()

Here is the simplified version of the one that does work.

import torch
import torch.nn as nn
import pygad.torchga
import pygad

class NN(nn.Module):
    def __init__(self, input_size, hidden_size, output_size):
        super().__init__()
        self.linear1 = nn.Linear(input_size, hidden_size)
        self.linear2 = nn.Linear(hidden_size, hidden_size)
        self.linear3 = nn.Linear(hidden_size, hidden_size)
        self.linear4 = nn.Linear(hidden_size, output_size)

    def forward(self, x):
        x = self.linear1(x)
        x = self.linear2(x)
        x = self.linear3(x)
        x = self.linear4(x)
        return x
    
def fitness_func(solution, solution_idx):
    return 0

def trainModel():

    NeuralNet = NN(1440, 1440, 3)

    torch_ga = pygad.torchga.TorchGA(model=NeuralNet, num_solutions=10)

    ga_instance = pygad.GA(num_generations=10,
                            num_parents_mating=2,
                            initial_population=torch_ga.population_weights,
                            fitness_func=fitness_func)

    ga_instance.run()

if __name__ == "__main__":
    trainModel()

Both of these should work the same but they don't

When you look at the pygad code you can see it's explicitly checking that the fitness function has exactly two parameters:

        # Check if the fitness function accepts 2 paramaters.
        if (fitness_func.__code__.co_argcount == 2):
            self.fitness_func = fitness_func
        else:
            self.valid_parameters = False
            raise ValueError("The fitness function must accept 2 parameters:\n1) A solution to calculate its fitness value.\n2) The solution's index within the population.\n\nThe passed fitness function named '{funcname}' accepts {argcount} parameter(s).".format(funcname=fitness_func.__code__.co_name, argcount=fitness_func.__code__.co_argcount))

So if you want to use it in a class you'll need to make it a static method so you aren't required to pass in self:

@staticmethod
def fitness_func(solution, solution_idx):
    return 0