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: Returns the inferred predictions of the network given a dataset ``x``, where the tensors are
  converted into Numpy arrays.
  
  Args:
      x (Union[~torch.Tensor, ~numpy.ndarray] or Union[tuple, list] of Union[~torch.Tensor, ~numpy.ndarray]):
          Input to the model. Union[Tensor, ndarray] if the model has a single input.
          Union[tuple, list] of Union[Tensor, ndarray] if the model has multiple inputs.
      checkpoint (Union[str, int]): Which model checkpoint weights to load for the prediction.
  
          - If 'best', will load the best weights according to ``monitor_metric`` and ``monitor_mode``.
          - If 'last', will load the last model checkpoint.
          - If int, will load the checkpoint of the specified epoch.
          - If a path (str), will load the model pickled state_dict weights (for instance, saved as
            ``torch.save(a_pytorch_network.state_dict(), "./a_path.p")``).
  
          This argument has no effect when logging is disabled. (Default value = 'best')
      kwargs: Any keyword arguments to pass to :func:`~Model.predict()`.
  
  Returns:
      Return the predictions in the format outputted by the model.
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code: def infer_data(self, x, **kwargs) -> Any:
      """
      Returns the inferred predictions of the network given a dataset ``x``, where the tensors are
      converted into Numpy arrays.
  
      Args:
          x (Union[~torch.Tensor, ~numpy.ndarray] or Union[tuple, list] of Union[~torch.Tensor, ~numpy.ndarray]):
              Input to the model. Union[Tensor, ndarray] if the model has a single input.
              Union[tuple, list] of Union[Tensor, ndarray] if the model has multiple inputs.
          checkpoint (Union[str, int]): Which model checkpoint weights to load for the prediction.
  
              - If 'best', will load the best weights according to ``monitor_metric`` and ``monitor_mode``.
              - If 'last', will load the last model checkpoint.
              - If int, will load the checkpoint of the specified epoch.
              - If a path (str), will load the model pickled state_dict weights (for instance, saved as
                ``torch.save(a_pytorch_network.state_dict(), "./a_path.p")``).
  
              This argument has no effect when logging is disabled. (Default value = 'best')
          kwargs: Any keyword arguments to pass to :func:`~Model.predict()`.
  
      Returns:
          Return the predictions in the format outputted by the model.
      """
      return self._predict(self.model.predict, x, **kwargs)
  
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 35 for calling the ModelBundle obj's underlying member method code with example usage: obj.infer_data(x) and returns: self