Project: s41467-024-46879-4 | Function: computeLossAcc (python)

system: You are an expert Python programmer implementing algorithms from scientific papers.
Complete the masked function based on the algorithm description.
Follow Python best practices and maintain consistent code style.
Return **only** the complete Python function code.

user: Complete this Python function:

**DESCRIPTION:** Implements the Mean Square Error cost function and classification accuracy computation for Equilibrium Propagation training on Ising machines with binary spins and multiple output neurons per class

**KEY TERMS:** {
  "seq[1]": "output layer activations σʸ from Ising spins",
  "target": "target output states σ̂ʸ",
  "expand_output": "number of neurons per output class (4 per digit class)",
  "pred_ave": "averaged predictions across multiple output neurons",
  "loss": "Mean Square Error between spin states and targets",
  "pred": "classification accuracy based on argmax over averaged outputs"
}

**CONTEXT CODE:** import torch.nn as nn
import numpy as np
import matplotlib.pyplot as plt
import torch.nn.functional as F
import math
import torch

class Network(nn.Module):

    def __init__(self, args):

        super(Network, self).__init__()

        self.sign_beta = 1
        with torch.no_grad():
            N_inputs, N_hidden, N_output =  args.layersList[0], args.layersList[1], args.layersList[2]

            self.weights_0 = 2*(np.random.rand(N_inputs, N_hidden)-0.5)*math.sqrt(1/N_inputs)
            self.weights_1 = 2*(np.random.rand(N_hidden, N_output)-0.5)*math.sqrt(1/N_hidden)

            self.weights_0 = args.gain_weight0 * self.weights_0
            self.weights_1 = args.gain_weight1 * self.weights_1

            self.bias_0 = np.zeros(N_hidden)
            self.bias_1 = np.zeros(N_output)


    def computeLossAcc(self, seq, target, args, stage = 'training'):
        '''
        compute the loss and the error from s and seq (for any kind of sampler)
        '''
        # MASKED: Implements the Mean Square Error cost function and classification accuracy computation for Equilibrium Propagation training on Ising machines with binary spins and multiple output neurons per class implementation
        raise NotImplementedError("This function is masked for testing purposes.")


    def computeGrads(self, data, s, seq, args):
        with torch.no_grad():
            coef = self.sign_beta*args.beta*args.batch_size
            gradsW, gradsB = [], []

            gradsW.append(-(np.matmul(s[0].T, s[1]) - np.matmul(seq[0].T, seq[1])) /coef)
            gradsW.append(-(np.matmul(data.numpy().T, s[0]) - np.matmul(data.numpy().T, seq[0])) /coef)

            gradsB.append(-(s[1] - seq[1]).sum(0) /coef)
            gradsB.append(-(s[0] - seq[0]).sum(0) /coef)


            return gradsW, gradsB


    def updateParams(self, data, s, seq, args):
        with torch.no_grad():
            ## Compute gradients and update weights from simulated sampling
            gradsW, gradsB = self.computeGrads(data, s, seq, args)


            #weights
            assert self.weights_1.shape == gradsW[0].shape
            self.weights_1 += args.lrW0 * gradsW[0]
            self.weights_1 = self.weights_1.clip(-1,1)

            assert self.weights_0.shape == gradsW[1].shape
            self.weights_0 += args.lrW1 * gradsW[1]
            self.weights_0 = self.weights_0.clip(-1,1)

            #biases
            assert self.bias_1.shape == gradsB[0].shape
            self.bias_1 += args.lrB0 * gradsB[0]
            self.bias_1 = self.bias_1.clip(-1,1)

            assert self.bias_0.shape == gradsB[1].shape
            self.bias_0 += args.lrB1 * gradsB[1]
            self.bias_0 = self.bias_0.clip(-1,1)

            del gradsW, gradsB



Complete the function implementation replacing "raise NotImplementedError".