code: def update_fit_params(
      self,
      weights,
  ):
      self.updated_weights = weights
      filter_weights(self.updated_weights)
      self.preds_mapper = None
  
imports: import torch
  import numpy
  from torch.utils.data import DataLoader
  from pytorch_tabnet import tab_network
  from pytorch_tabnet.utils import create_explain_matrix, filter_weights, SparsePredictDataset, PredictDataset, check_input, create_group_matrix
  from torch.nn.utils import clip_grad_norm_
  from pytorch_tabnet.pretraining_utils import create_dataloaders, validate_eval_set
  from pytorch_tabnet.metrics import UnsupMetricContainer, check_metrics, UnsupervisedLoss
  from pytorch_tabnet.abstract_model import TabModel
  import scipy
label_desc: Use image node_id 4 for calling the TabNetPretrainer obj's underlying member method code with example usage: obj.update_fit_params(weights) without return types