code: def _compute_impt_smpl(
      self, model_propensities, logged_propensities
  ) -> Tuple[torch.Tensor, torch.Tensor]:
      logged_propensities = logged_propensities.reshape(-1, 1)
      assert (
          model_propensities.shape == logged_propensities.shape
          and len(model_propensities.shape) == 2
          and model_propensities.shape[1] == 1
      ), f"{model_propensities.shape} {logged_propensities.shape}"
  
      impt_smpl = model_propensities / logged_propensities
      clamped_impt_smpl = ips_clamp(impt_smpl, self.params.ips_clamp)
      return impt_smpl, clamped_impt_smpl
  
imports: import logging
  from typing import List, Optional, Tuple
  import reagent.core.types
  import torch
  import torch.nn
  import torch.nn.functional
  from reagent.core.dataclasses import field
  from reagent.core.parameters import Seq2SlateParameters
  from reagent.evaluation.evaluation_data_page import EvaluationDataPage
  from reagent.model_utils.seq2slate_utils import Seq2SlateMode
  from reagent.models.seq2slate import BaselineNet, Seq2SlateTransformerNet
  from reagent.optimizer.union import Optimizer__Union
  from reagent.training.ranking.helper import ips_clamp
  from reagent.training.reagent_lightning_module import ReAgentLightningModule
label_desc: Use image node_id 3 for calling the Seq2SlateTrainer obj's underlying member method code with example usage: obj._compute_impt_smpl(model_propensities, logged_propensities) and returns: impt_smpl,  clamped_impt_smpl