code: def predict_on_batch(self, X_batch):
      X_batch = torch.autograd.Variable(torch.cuda.FloatTensor(X_batch))
      outputs = self.forward(X_batch, training=False)
      y_pred_batch = torch.stack(outputs, 1).data.cpu().numpy()[:]
      y_pred_batch = np.squeeze(y_pred_batch, axis=2)
      return y_pred_batch
  
file_path: deepchem/contrib/torch/torch_multitask_regression.py
imports: import torch
  import numpy
  from torch_model import TorchMultitaskModel
label_desc: Use image node_id 5 for calling the TorchMultitaskRegression obj's underlying member method code with example usage: obj.predict_on_batch(X_batch) and returns: y_pred_batch