Project: s41467-024-46879-4 | Function: getBinState (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:** Converts continuous neuron states to binary activations for Equilibrium Propagation training, bridging the gap between standard EP continuous neurons and Ising machine binary spins

**KEY TERMS:** {
  "states": "continuous pre-activation values sᵢ from EP dynamics",
  "bin_states": "binary spin states σᵢ for Ising machine compatibility",
  "rho_threshold": "activation threshold for binary conversion",
  "rho": "activation function (sign function for binary outputs)",
  "layer": "network layer index for state processing"
}

**CONTEXT CODE:** from scipy import*
import torch
import torch.nn as nn
import numpy as np
import matplotlib.pyplot as plt

from activation_functions import rho, rhop, rhop1, rhop2

class Network(nn.Module):
    ''' Define the network studied
    '''
    def __init__(self, args):

        super(Network, self).__init__()

        self.T = args.T
        self.Kmax = args.Kmax
        self.beta = torch.tensor(args.beta)
        self.gamma_neur = args.gamma_neur
        self.clamped = args.clamped
        self.batchSize = args.batchSize

        self.neuronMin = 0
        self.neuronMax = 1

        self.activationFun = args.activationFun
        self.epoch = 0

        self.W = nn.ModuleList(None)
        with torch.no_grad():
            for i in range(len(args.layersList)-1):
                self.W.extend([nn.Linear(args.layersList[i+1], args.layersList[i], bias = True)])

        if args.device >= 0 and torch.cuda.is_available():
            self.device = torch.device("cuda:"+str(args.device))
            self.cuda = True
        else:
            self.device = torch.device("cpu")
            self.cuda = False


    def getBinState(self, states, args):
        '''
        Return the binary states from pre-activations stored in 'states'
        '''
        # MASKED: Converts continuous neuron states to binary activations for Equilibrium Propagation training, bridging the gap between standard EP continuous neurons and Ising machine binary spins implementation
        raise NotImplementedError("This function is masked for testing purposes.")


    def stepper(self, args, s, seq = None, target = None, beta = 0):
        '''
        EP based model - prototypical settings, ie not energy based dynamics!
        with moving average filter for pre-activations
        '''
        pre_act = s.copy()
        bin_states = self.getBinState(s, args)

        #computing pre-activation for every layer weights + bias
        pre_act[0] = self.W[0](bin_states[1])
        pre_act[0] = rhop(s[0] - args.rho_threshold)*pre_act[0]
        if beta != 0:
            pre_act[0] = pre_act[0] + beta*(target-s[0])

        for layer in range(1, len(s)-1):
            #previous layer contribution: weights + bias
            pre_act[layer] =  self.W[layer](bin_states[layer+1])
            # next layer contribution
            pre_act[layer] += torch.mm(bin_states[layer-1], self.W[layer-1].weight)
            #multiply with Indicatrice(pre-activation)
            pre_act[layer] = rhop(s[layer] - args.rho_threshold)*pre_act[layer]

        #updating each accumulated pre-activation
        for layer in range(len(s)-1):
            #moving average filter for the pre_activations
            s[layer] =  (1-self.gamma_neur)*s[layer] + self.gamma_neur*pre_act[layer]

            #clamping on pre-activations
            s[layer] = s[layer].clamp(self.neuronMin, self.neuronMax)


        return s


    def forward(self, args, s, seq = None,  beta = 0, target = None, tracking = False):
        '''
        Relaxation function
        '''
        T, Kmax = self.T, self.Kmax
        n_track = 10
        h, y = [[] for k in range(n_track)], [[] for k in range(n_track)]

        with torch.no_grad():
            if beta == 0:
                # Free phase
                for t in range(T):
                    s = self.stepper(args, s)

                    if tracking:
                        for k in range(n_track):
                            y[k].append(s[0][k][2*k].item())
                            h[k].append(s[1][k][2*k].item())
            else:
                # Nudged phase
                for t in range(Kmax):
                    s = self.stepper(args,s, target = target, beta = beta, seq = seq)

                    if tracking:
                        for k in range(n_track):
                            y[k].append(s[0][k][2*k].item())
                            h[k].append(s[1][k][2*k].item())
        if tracking:
            return s, y, h
        else:
            return s


    def computeGradients(self, args, s, seq, method = None):
        '''
        Compute EQ gradient to update the synaptic weight
        '''
        batch_size = s[0].size(0)

        coef = 1/(self.beta*batch_size)
        gradW, gradBias, gradAlpha = [], [], []

        with torch.no_grad():
            bin_states_0, bin_states_1 = self.getBinState(seq, args), self.getBinState(s, args)

            for layer in range(len(s)-1):
                gradW.append( coef * (torch.mm(torch.transpose(bin_states_1[layer], 0, 1), bin_states_1[layer+1]) - torch.mm(torch.transpose(bin_states_0[layer], 0, 1), bin_states_0[layer+1])))
                gradBias.append( coef * (bin_states_1[layer] -  bin_states_0[layer]).sum(0))

        return gradW, gradBias


    def updateWeight(self, epoch, s, seq, args):
        '''
        Update weights with SGD
        '''

        gradW, gradBias = self.computeGradients(args, s, seq)


Complete the function implementation replacing "raise NotImplementedError".