class_docstr: Class for TabNet model.
class_docstr_tok: ["Class","for","TabNet","model","."]
docstr: Set attributes relative to fit function.
  
  Parameters
  ----------
  X_train : np.ndarray
      Train set
  y_train : np.array
      Train targets
  eval_set : list of tuple
      List of eval tuple set (X, y).
  weights : bool or dictionnary
      0 for no balancing
      1 for automated balancing
docstr_tok: ["Set","attributes","relative","to","fit","function",".","Parameters","--","--","--","--","--","X_train",":","np.ndarray","Train","set","y_train",":","np.array","Train","targets","eval_set",":","list","of","tuple","List","of","eval","tuple","set","(","X",",","y",")",".","weights",":","bool","or","dictionnary","0","for","no","balancing","1","for","automated","balancing"]
code: def update_fit_params(self, X_train, y_train, eval_set, weights):
      """
      Set attributes relative to fit function.
  
      Parameters
      ----------
      X_train : np.ndarray
          Train set
      y_train : np.array
          Train targets
      eval_set : list of tuple
          List of eval tuple set (X, y).
      weights : bool or dictionnary
          0 for no balancing
          1 for automated balancing
      """
      raise NotImplementedError(
          "users must define update_fit_params to use this base class"
      )
  
imports: from dataclasses import dataclass, field
  from typing import List, Any, Dict
  import torch
  from torch.nn.utils import clip_grad_norm_
  import numpy
  from scipy.sparse import csc_matrix
  from abc import abstractmethod
  from pytorch_tabnet import tab_network
  from pytorch_tabnet.utils import SparsePredictDataset, PredictDataset, create_explain_matrix, validate_eval_set, create_dataloaders, define_device, ComplexEncoder, check_input, check_warm_start, create_group_matrix, check_embedding_parameters
  from pytorch_tabnet.callbacks import CallbackContainer, History, EarlyStopping, LRSchedulerCallback
  from pytorch_tabnet.metrics import MetricContainer, check_metrics
  from sklearn.base import BaseEstimator
  from torch.utils.data import DataLoader
  import io
  import json
  from pathlib import Path
  import shutil
  import zipfile
  import warnings
  import copy
  import scipy
label_desc: Use image node_id 21 for calling the TabModel obj's underlying member method code with example usage: obj.update_fit_params(X_train, y_train, eval_set, weights) without return types