code: def update_fit_params(self, X_train, y_train, eval_set, weights):
      if len(y_train.shape) != 2:
          msg = (
              "Targets should be 2D : (n_samples, n_regression) "
              + f"but y_train.shape={y_train.shape} given.\n"
              + "Use reshape(-1, 1) for single regression."
          )
          raise ValueError(msg)
      self.output_dim = y_train.shape[1]
      self.preds_mapper = None
  
      self.updated_weights = weights
      filter_weights(self.updated_weights)
  
imports: import torch
  import numpy
  from scipy.special import softmax
  from pytorch_tabnet.utils import SparsePredictDataset, PredictDataset, filter_weights
  from pytorch_tabnet.abstract_model import TabModel
  from pytorch_tabnet.multiclass_utils import infer_output_dim, check_output_dim
  from torch.utils.data import DataLoader
  import scipy
label_desc: Use image node_id 4 for calling the TabNetRegressor obj's underlying member method code with example usage: obj.update_fit_params(X_train, y_train, eval_set, weights) without return types