class_docstr: Class for TabNet model.
class_docstr_tok: ["Class","for","TabNet","model","."]
docstr: Compute the loss.
  
  Parameters
  ----------
  y_score : a :tensor: `torch.Tensor`
      Score matrix
  y_true : a :tensor: `torch.Tensor`
      Target matrix
  
  Returns
  -------
  float
      Loss value
docstr_tok: ["Compute","the","loss",".","Parameters","--","--","--","--","--","y_score",":","a",":","tensor",":","`","torch.Tensor","`","Score","matrix","y_true",":","a",":","tensor",":","`","torch.Tensor","`","Target","matrix","Returns","--","--","--","-","float","Loss","value"]
code: def compute_loss(self, y_score, y_true):
      """
      Compute the loss.
  
      Parameters
      ----------
      y_score : a :tensor: `torch.Tensor`
          Score matrix
      y_true : a :tensor: `torch.Tensor`
          Target matrix
  
      Returns
      -------
      float
          Loss value
      """
      raise NotImplementedError(
          "users must define compute_loss 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 22 for calling the TabModel obj's underlying member method code with example usage: obj.compute_loss(y_score, y_true) without return types