class_docstr: Class for TabNet model.
class_docstr_tok: ["Class","for","TabNet","model","."]
code: def __post_init__(self):
      # These are default values needed for saving model
      self.batch_size = 1024
      self.virtual_batch_size = 128
  
      torch.manual_seed(self.seed)
      # Defining device
      self.device = torch.device(define_device(self.device_name))
      if self.verbose != 0:
          warnings.warn(f"Device used : {self.device}")
  
      # create deep copies of mutable parameters
      self.optimizer_fn = copy.deepcopy(self.optimizer_fn)
      self.scheduler_fn = copy.deepcopy(self.scheduler_fn)
  
      updated_params = check_embedding_parameters(
          self.cat_dims, self.cat_idxs, self.cat_emb_dim
      )
      self.cat_dims, self.cat_idxs, self.cat_emb_dim = updated_params
  
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 1 for calling the TabModel obj's underlying member method code with example usage: obj.__post_init__() without return types