code: def validate(self):
      assert len(self.logged_propensities.shape) == 2
      assert len(self.logged_rewards.shape) == 2
      assert len(self.logged_values.shape) == 2
      assert len(self.model_propensities.shape) == 2
      assert len(self.model_rewards.shape) == 2
      assert len(self.model_values.shape) == 2
  
      assert self.logged_propensities.shape[1] == 1
      assert self.logged_rewards.shape[1] == 1
      assert self.logged_values.shape[1] == 1
      num_actions = self.model_propensities.shape[1]
      assert self.model_rewards.shape[1] == num_actions
      assert self.model_values.shape[1] == num_actions
  
      assert self.action_mask.shape == self.model_propensities.shape
  
      if self.logged_metrics is not None:
          assert len(self.logged_metrics.shape) == 2
          assert len(self.logged_metrics_values.shape) == 2
          assert len(self.model_metrics.shape) == 2
          assert len(self.model_metrics_values.shape) == 2
  
          num_metrics = self.logged_metrics.shape[1]
          assert self.logged_metrics_values.shape[1] == num_metrics, (
              "Invalid shape: "
              + str(self.logged_metrics_values.shape)
              + " != "
              + str(num_metrics)
          )
          assert (
              self.model_metrics.shape[1] == num_metrics * num_actions
          ), (
              "Invalid shape: "
              + str(self.model_metrics.shape)
              + " != "
              + str(num_metrics * num_actions)
          )
          assert (
              self.model_metrics_values.shape[1]
              == num_metrics * num_actions
          )
  
      minibatch_size = self.logged_propensities.shape[0]
      logger.info(
          "EvaluationDataPage data size: {}".format(minibatch_size)
      )
      assert minibatch_size == self.logged_rewards.shape[0]
      assert minibatch_size == self.logged_values.shape[0]
      assert minibatch_size == self.model_propensities.shape[0]
      assert minibatch_size == self.model_rewards.shape[0]
      assert minibatch_size == self.model_values.shape[0]
      if self.logged_metrics is not None:
          assert minibatch_size == self.logged_metrics.shape[0]
          assert minibatch_size == self.logged_metrics_values.shape[0]
          assert minibatch_size == self.model_metrics.shape[0]
          assert minibatch_size == self.model_metrics_values.shape[0]
  
      logger.info(
          "Average logged reward = %s", self.logged_rewards.mean()
      )
      logger.info(
          "Average model propensity for action 0 = %s",
          self.model_propensities[:, 0].mean(),
      )
      logger.info(
          "Average model propensity for action 1 = %s",
          self.model_propensities[:, 1].mean(),
      )
      logger.info(
          "Average logged propensity = %s",
          self.logged_propensities.mean(),
      )
  
      flatten_mdp_id = self.mdp_id.reshape(-1)
      unique_mdp_ids = set(flatten_mdp_id.tolist())
      prev_mdp_id, prev_seq_num = None, None
      mdp_count = 0
      for mdp_id, seq_num in zip(flatten_mdp_id, self.sequence_number):
          if prev_mdp_id is None or mdp_id != prev_mdp_id:
              mdp_count += 1
              prev_mdp_id = mdp_id
          else:
              assert seq_num > prev_seq_num, (
                  f"For mdp_id {mdp_id}, got {seq_num} <= {prev_seq_num}."
                  f"Sequence number must be in increasing order.\n"
                  f"Zip(mdp_id, seq_num): "
                  f"{list(zip(flatten_mdp_id, self.sequence_number))}"
              )
          prev_seq_num = seq_num
  
      assert (
          len(unique_mdp_ids) == mdp_count
      ), "MDPs are broken up. {} vs {}".format(
          len(unique_mdp_ids), mdp_count
      )
  
imports: from __future__ import annotations
  import logging
  import math
  from dataclasses import dataclass, fields
  from typing import cast, Optional, TYPE_CHECKING
  import numpy
  import torch
  import torch.nn
  from reagent.core import types
  from reagent.core.torch_utils import masked_softmax
  from reagent.model_utils.seq2slate_utils import Seq2SlateMode
  from reagent.models.seq2slate import Seq2SlateTransformerNet
label_desc: Use image node_id 9 for calling the EvaluationDataPage obj's underlying member method code with example usage: obj.validate() without return types