docstr: Make predictions on a batch (valid)
  
  Parameters
  ----------
  X : a :tensor: `torch.Tensor` or matrix: `scipy.sparse.csr_matrix`
      Input data
  
  Returns
  -------
  predictions : np.array
      Predictions of the regression problem
docstr_tok: ["Make","predictions","on","a","batch","(","valid",")","Parameters","--","--","--","--","--","X",":","a",":","tensor",":","`","torch.Tensor","`","or","matrix",":","`","scipy.sparse.csr_matrix","`","Input","data","Returns","--","--","--","-","predictions",":","np.array","Predictions","of","the","regression","problem"]
code: def predict(self, X):
      """
      Make predictions on a batch (valid)
  
      Parameters
      ----------
      X : a :tensor: `torch.Tensor` or matrix: `scipy.sparse.csr_matrix`
          Input data
  
      Returns
      -------
      predictions : np.array
          Predictions of the regression problem
      """
      self.network.eval()
  
      if scipy.sparse.issparse(X):
          dataloader = DataLoader(
              SparsePredictDataset(X),
              batch_size=self.batch_size,
              shuffle=False,
          )
      else:
          dataloader = DataLoader(
              PredictDataset(X),
              batch_size=self.batch_size,
              shuffle=False,
          )
  
      results = []
      embedded_res = []
      for batch_nb, data in enumerate(dataloader):
          data = data.to(self.device).float()
          output, embeded_x, _ = self.network(data)
          predictions = output.cpu().detach().numpy()
          results.append(predictions)
          embedded_res.append(embeded_x.cpu().detach().numpy())
      res_output = np.vstack(results)
      embedded_inputs = np.vstack(embedded_res)
      return res_output, embedded_inputs
  
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 15 for calling the TabNetPretrainer obj's underlying member method code with example usage: obj.predict(X) and returns: res_output,  embedded_inputs