class_docstr: The :class:`~poutyne.ModelBundle` class provides a straightforward experimentation tool for efficient and entirely
  customizable finetuning of the whole neural network training procedure with PyTorch. The
  :class:`~poutyne.ModelBundle` object takes care of the training and testing processes while also managing to keep
  traces of all pertinent information via the automatic logging option.
  
  Use ``ModelBundle.from_*`` methods to instanciate a :class:`~poutyne.ModelBundle`.
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docstr: Compare the results of the ModelBundle with another model bundle. To compare, both ModelBundles need to be
  logged, monitor the same metric and the same monitor mode ("min" or "max").
  
  Args:
      another_model_bundle (~poutyne.ModelBundle): Another Poutyne model bundle to compare results with.
  
  Return:
      Whether the ModelBundle is better than the ModelBundle to compare with.
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code: def is_better_than(self, another_model_bundle) -> bool:
      """
      Compare the results of the ModelBundle with another model bundle. To compare, both ModelBundles need to be
      logged, monitor the same metric and the same monitor mode ("min" or "max").
  
      Args:
          another_model_bundle (~poutyne.ModelBundle): Another Poutyne model bundle to compare results with.
  
      Return:
          Whether the ModelBundle is better than the ModelBundle to compare with.
      """
      if not self.logging:
          raise ValueError("The model bundle is not logged.")
      if not another_model_bundle.logging:
          raise ValueError(
              "The model bundle to compare to is not logged."
          )
  
      if self.monitor_metric != another_model_bundle.monitor_metric:
          raise ValueError(
              "The monitored metric is not the same between the two model bundles."
          )
      monitored_metric = self.monitor_metric
  
      if self.monitor_mode != another_model_bundle.monitor_mode:
          raise ValueError(
              "The monitored mode is not the same between the two model bundles."
          )
      monitor_mode = self.monitor_mode
  
      checkpoint = "best" if self.monitoring else "last"
      self_stats = self.load_checkpoint(checkpoint, verbose=False)
      self_monitored_metric = self_stats[monitored_metric]
      self_monitored_metric_value = self_monitored_metric.item()
  
      other_checkpoint = (
          "best" if another_model_bundle.monitoring else "last"
      )
      other_stats = self.load_checkpoint(
          other_checkpoint, verbose=False
      )
      other_monitored_metric = other_stats[monitored_metric]
      other_monitored_metric_value = other_monitored_metric.item()
  
      if monitor_mode == "min":
          is_better_than = (
              self_monitored_metric_value < other_monitored_metric_value
          )
      else:
          is_better_than = (
              self_monitored_metric_value > other_monitored_metric_value
          )
      return is_better_than
  
imports: import os
  import warnings
  from typing import Any, Callable, Dict, List, Tuple, Union
  import torch
  from poutyne.framework.callbacks import AtomicCSVLogger, BestModelRestore, LRSchedulerCheckpoint, ModelCheckpoint, OptimizerCheckpoint, PeriodicSaveLambda, RandomStatesCheckpoint, TensorBoardLogger
  from poutyne.framework.model import Model
  from poutyne.plotting import plot_history
  from poutyne.utils import load_random_states, set_seeds
label_desc: Use image node_id 37 for calling the ModelBundle obj's underlying member method code with example usage: obj.is_better_than(another_model_bundle) and returns: is_better_than