code: def _l1_updator(self, model_weights, gradient, fit_intercept, lr):
      if fit_intercept:
          gradient_without_intercept = gradient[:-1]
          coef_ = model_weights[:-1]
      else:
          gradient_without_intercept = gradient
          coef_ = model_weights
  
      new_weights = torch.sign(
          coef_ - gradient_without_intercept
      ) * torch.maximum(
          torch.tensor([0]),
          torch.abs(coef_ - gradient_without_intercept)
          - self.shrinkage_val(lr),
      )
  
      if fit_intercept:
          new_intercept = model_weights[-1] - gradient[-1]
          new_weights = torch.concat(
              (new_weights, new_intercept.reshape((1, 1)))
          )
  
      return new_weights
  
imports: import logging
  import numpy
  import torch
label_desc: Use image node_id 9 for calling the Optimizer obj's underlying member method code with example usage: obj._l1_updator(model_weights, gradient, fit_intercept, lr) and returns: new_weights