code: def compute_batch(self, duplicate_manager=None, context_manager=None):
          
          from ...acquisitions import AcquisitionLP
          assert isinstance(self.acquisition, AcquisitionLP)
  
          self.acquisition.update_batches(None,None,None)
  
          # --- GET first element in the batch
          X_batch = self.acquisition.optimize()[0]
          k=1
  
          if self.batch_size >1:
              # ---------- Approximate the constants of the the method
              L = estimate_L(self.acquisition.model.model,self.acquisition.space.get_bounds())
              Min = self.acquisition.model.model.Y.min()
  
          # --- GET the remaining elements
          while k<self.batch_size:
              self.acquisition.update_batches(X_batch,L,Min)
              new_sample = self.acquisition.optimize()[0]
              X_batch = np.vstack((X_batch,new_sample))
              k +=1
  
          # --- Back to the non-penalized acquisition
          self.acquisition.update_batches(None,None,None)
          return X_batch
docs: Computes the elements of the batch sequentially by penalizing the acquisition.