text: import torch
  from torch.nn import functional as F
  from importlib import import_module
  from torch.optim import AdamW
  from torch.distributions.kl import kl_divergence
  
  from agoge import AbstractSolver
  
  from .utils import sigmoidal_annealing
  
  class DX7NeuralSylvesterProcess(AbstractSolver):
   
      def __init__(self, model,
          Optim=AdamW, optim_opts=dict(lr= 1e-4),
          max_beta=0.5,
          beta_temp=1e-4,
          **kwargs):
  
          if isinstance(Optim, str):
              Optim = import_module(Optim)
  
          self.optim = Optim(params=model.parameters(), **optim_opts)
          self.max_beta = max_beta
          self.model = model
  
          self.iter = 0
          self.beta_temp = beta_temp
  
      def loss(self, X, X_hat, X_a, flow_context, flow_target):
  
          valid_predictions = (~X_a).nonzero().t()
  
          valid_x_hat = X_hat[(*valid_predictions,)]
          valid_x = X[(*valid_predictions,)]
  
          p_z = flow_target.q_z.log_prob(flow_context.z_k).sum(-1)
          q_z = flow_context.q_z.log_prob(flow_context.z_0).sum(-1)
          kl = (q_z-p_z-flow_context.log_det).mean() / flow_context.z_k.shape[-1]
          beta = sigmoidal_annealing(self.iter, self.beta_temp).item()
  
          reconstruction_loss = F.cross_entropy(valid_x_hat, valid_x)
          accuracy = (valid_x_hat.argmax(-1)==valid_x).float().mean()
  
          loss = reconstruction_loss + self.max_beta * beta * kl
  
          return loss, {
              'accuracy': accuracy,
              'reconstruction_loss': reconstruction_loss,
              'kl': kl,
              'beta': beta,
              'q_log_det': flow_context.log_det.mean(),
              # 'p_log_det': flow_target.log_det.mean(),
              'q_z': q_z.mean(),
              'p_z': p_z.mean()
          }
          
  
      def solve(self, X, **kwargs):
          
          Y = self.model(**X)
          loss, L = self.loss(**X, **Y)
  
          if loss != loss:
              raise ValueError('Nan Values detected')
  
          if self.model.training:
              self.iter += 1
              self.optim.zero_grad()
              loss.backward()
              self.optim.step()
  
          return L
  
      
      def step(self):
  
          pass
  
  
      def state_dict(self):
          
          state_dict = {
              'optim': self.optim.state_dict(),
              'iter': self.iter
          }
  
          return state_dict
  
      def load_state_dict(self, state_dict):
          
          self.optim.load_state_dict(state_dict['optim'])
          self.iter = state_dict['iter']