class_docstr: Class for TabNet model.
class_docstr_tok: ["Class","for","TabNet","model","."]
code: def load_class_attrs(self, class_attrs):
      for attr_name, attr_value in class_attrs.items():
          setattr(self, attr_name, attr_value)
  
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 7 for calling the TabModel obj's underlying member method code with example usage: obj.load_class_attrs(class_attrs) without return types