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()
      return self.network(X)
  
imports: import torch
  import numpy
  from torch.utils.data import DataLoader
  from pytorch_tabnet import tab_network
  from pytorch_tabnet.utils import create_explain_matrix, filter_weights, SparsePredictDataset, PredictDataset, check_input, create_group_matrix
  from torch.nn.utils import clip_grad_norm_
  from pytorch_tabnet.pretraining_utils import create_dataloaders, validate_eval_set
  from pytorch_tabnet.metrics import UnsupMetricContainer, check_metrics, UnsupervisedLoss
  from pytorch_tabnet.abstract_model import TabModel
  import scipy
label_desc: Use image node_id 13 for calling the TabNetPretrainer obj's underlying member method code with example usage: obj._predict_batch(X) and returns: self