class_docstr: Class for TabNet model.
class_docstr_tok: ["Class","for","TabNet","model","."]
docstr: Predict one batch of data.
  
  Parameters
  ----------
  X : torch.Tensor
      Owned products
  
  Returns
  -------
  np.array
      model scores
docstr_tok: ["Predict","one","batch","of","data",".","Parameters","--","--","--","--","--","X",":","torch.Tensor","Owned","products","Returns","--","--","--","-","np.array","model","scores"]
code: def _predict_batch(self, X):
      """
      Predict one batch of data.
  
      Parameters
      ----------
      X : torch.Tensor
          Owned products
  
      Returns
      -------
      np.array
          model scores
      """
      X = X.to(self.device).float()
  
      # compute model output
      scores, _ = self.network(X)
  
      if isinstance(scores, list):
          scores = [x.cpu().detach().numpy() for x in scores]
      else:
          scores = scores.cpu().detach().numpy()
  
      return scores
  
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 13 for calling the TabModel obj's underlying member method code with example usage: obj._predict_batch(X) and returns: scores