docstr: An implementation of the N-HiTS model, as presented in [1]_.
  
  N-HiTS is similar to N-BEATS (implemented in :class:`NBEATSModel`),
  but attempts to provide better performance at lower computational cost by introducing
  multi-rate sampling of the inputs and multi-scale interpolation of the outputs.
  
  Similar to :class:`NBEATSModel`, in addition to the univariate version presented in the paper,
  this implementation also supports multivariate series (and covariates) by flattening the model inputs
  to a 1-D series and reshaping the outputs to a tensor of appropriate dimensions. Furthermore, it also
  supports producing probabilistic forecasts (by specifying a `likelihood` parameter).
  
  This model supports past covariates (known for `input_chunk_length` points before prediction time).
  
  The multi-rate sampling is done via MaxPooling, which is controlled by ``pooling_kernel_sizes``.
  This parameter can be a tuple of tuples, of size (num_stacks x num_blocks), specifying the kernel
  size for each block in each stack. If left to ``None``, some default values will be used based on
  ``input_chunk_length``.
  Similarly, the multi-scale interpolation is controlled by ``n_freq_downsample``, which gives the
  downsampling factors to be used in each block of each stack. If left to ``None``, some default
  values will be used based on the ``output_chunk_length``.
  
  Parameters
  ----------
  input_chunk_length
      Number of time steps in the past to take as a model input (per chunk). Applies to the target
      series, and past and/or future covariates (if the model supports it).
  output_chunk_length
      Number of time steps predicted at once (per chunk) by the internal model. Also, the number of future values
      from future covariates to use as a model input (if the model supports future covariates). It is not the same
      as forecast horizon `n` used in `predict()`, which is the desired number of prediction points generated
      using either a one-shot- or auto-regressive forecast. Setting `n <= output_chunk_length` prevents
      auto-regression. This is useful when the covariates don't extend far enough into the future, or to prohibit
      the model from using future values of past and / or future covariates for prediction (depending on the
      model's covariate support).
  num_stacks
      The number of stacks that make up the whole model.
  num_blocks
      The number of blocks making up every stack.
  num_layers
      The number of fully connected layers preceding the final forking layers in each block of every stack.
  layer_widths
      Determines the number of neurons that make up each fully connected layer in each block of every stack.
      If a list is passed, it must have a length equal to `num_stacks` and every entry in that list corresponds
      to the layer width of the corresponding stack. If an integer is passed, every stack will have blocks
      with FC layers of the same width.
  pooling_kernel_sizes
      If set, this parameter must be a tuple of tuples, of size (num_stacks x num_blocks), specifying the kernel
      size for each block in each stack used for the input pooling layer.
      If left to ``None``, some default values will be used based on ``input_chunk_length``.
  n_freq_downsample
      If set, this parameter must be a tuple of tuples, of size (num_stacks x num_blocks), specifying the
      downsampling factors before interpolation, for each block in each stack.
      If left to ``None``, some default values will be used based on ``output_chunk_length``.
  dropout
      The dropout probability to be used in fully connected layers. This is compatible with Monte Carlo dropout
      at inference time for model uncertainty estimation (enabled with ``mc_dropout=True`` at
      prediction time).
  activation
      The activation function of encoder/decoder intermediate layer (default='ReLU').
      Supported activations: ['ReLU','RReLU', 'PReLU', 'Softplus', 'Tanh', 'SELU', 'LeakyReLU',  'Sigmoid']
  MaxPool1d
      Use MaxPool1d pooling. False uses AvgPool1d
  **kwargs
      Optional arguments to initialize the pytorch_lightning.Module, pytorch_lightning.Trainer, and
      Darts' :class:`TorchForecastingModel`.
  
  loss_fn
      PyTorch loss function used for training.
      This parameter will be ignored for probabilistic models if the ``likelihood`` parameter is specified.
      Default: ``torch.nn.MSELoss()``.
  likelihood
      One of Darts' :meth:`Likelihood <darts.utils.likelihood_models.Likelihood>` models to be used for
      probabilistic forecasts. Default: ``None``.
  torch_metrics
      A torch metric or a ``MetricCollection`` used for evaluation. A full list of available metrics can be found
      at https://torchmetrics.readthedocs.io/en/latest/. Default: ``None``.
  optimizer_cls
      The PyTorch optimizer class to be used. Default: ``torch.optim.Adam``.
  optimizer_kwargs
      Optionally, some keyword arguments for the PyTorch optimizer (e.g., ``{'lr': 1e-3}``
      for specifying a learning rate). Otherwise, the default values of the selected ``optimizer_cls``
      will be used. Default: ``None``.
  lr_scheduler_cls
      Optionally, the PyTorch learning rate scheduler class to be used. Specifying ``None`` corresponds
      to using a constant learning rate. Default: ``None``.
  lr_scheduler_kwargs
      Optionally, some keyword arguments for the PyTorch learning rate scheduler. Default: ``None``.
  use_reversible_instance_norm
      Whether to use reversible instance normalization `RINorm` against distribution shift as shown in [2]_.
      It is only applied to the features of the target series and not the covariates.
  batch_size
      Number of time series (input and output sequences) used in each training pass. Default: ``32``.
  n_epochs
      Number of epochs over which to train the model. Default: ``100``.
  model_name
      Name of the model. Used for creating checkpoints and saving tensorboard data. If not specified,
      defaults to the following string ``"YYYY-mm-dd_HH_MM_SS_torch_model_run_PID"``, where the initial part
      of the name is formatted with the local date and time, while PID is the processed ID (preventing models
      spawned at the same time by different processes to share the same model_name). E.g.,
      ``"2021-06-14_09_53_32_torch_model_run_44607"``.
  work_dir
      Path of the working directory, where to save checkpoints and Tensorboard summaries.
      Default: current working directory.
  log_tensorboard
      If set, use Tensorboard to log the different parameters. The logs will be located in:
      ``"{work_dir}/darts_logs/{model_name}/logs/"``. Default: ``False``.
  nr_epochs_val_period
      Number of epochs to wait before evaluating the validation loss (if a validation
      ``TimeSeries`` is passed to the :func:`fit()` method). Default: ``1``.
  force_reset
      If set to ``True``, any previously-existing model with the same name will be reset (all checkpoints will
      be discarded). Default: ``False``.
  save_checkpoints
      Whether to automatically save the untrained model and checkpoints from training.
      To load the model from checkpoint, call :func:`MyModelClass.load_from_checkpoint()`, where
      :class:`MyModelClass` is the :class:`TorchForecastingModel` class that was used (such as :class:`TFTModel`,
      :class:`NBEATSModel`, etc.). If set to ``False``, the model can still be manually saved using
      :func:`save()` and loaded using :func:`load()`. Default: ``False``.
  add_encoders
      A large number of past and future covariates can be automatically generated with `add_encoders`.
      This can be done by adding multiple pre-defined index encoders and/or custom user-made functions that
      will be used as index encoders. Additionally, a transformer such as Darts' :class:`Scaler` can be added to
      transform the generated covariates. This happens all under one hood and only needs to be specified at
      model creation.
      Read :meth:`SequentialEncoder <darts.dataprocessing.encoders.SequentialEncoder>` to find out more about
      ``add_encoders``. Default: ``None``. An example showing some of ``add_encoders`` features:
  
      .. highlight:: python
      .. code-block:: python
  
          def encode_year(idx):
              return (idx.year - 1950) / 50
  
          add_encoders={
              'cyclic': {'future': ['month']},
              'datetime_attribute': {'future': ['hour', 'dayofweek']},
              'position': {'past': ['relative'], 'future': ['relative']},
              'custom': {'past': [encode_year]},
              'transformer': Scaler(),
              'tz': 'CET'
          }
      ..
  random_state
      Control the randomness of the weights initialization. Check this
      `link <https://scikit-learn.org/stable/glossary.html#term-random_state>`_ for more details.
      Default: ``None``.
  pl_trainer_kwargs
      By default :class:`TorchForecastingModel` creates a PyTorch Lightning Trainer with several useful presets
      that performs the training, validation and prediction processes. These presets include automatic
      checkpointing, tensorboard logging, setting the torch device and more.
      With ``pl_trainer_kwargs`` you can add additional kwargs to instantiate the PyTorch Lightning trainer
      object. Check the `PL Trainer documentation
      <https://pytorch-lightning.readthedocs.io/en/stable/common/trainer.html>`_ for more information about the
      supported kwargs. Default: ``None``.
      Running on GPU(s) is also possible using ``pl_trainer_kwargs`` by specifying keys ``"accelerator",
      "devices", and "auto_select_gpus"``. Some examples for setting the devices inside the ``pl_trainer_kwargs``
      dict:
  
      - ``{"accelerator": "cpu"}`` for CPU,
      - ``{"accelerator": "gpu", "devices": [i]}`` to use only GPU ``i`` (``i`` must be an integer),
      - ``{"accelerator": "gpu", "devices": -1, "auto_select_gpus": True}`` to use all available GPUS.
  
      For more info, see here:
      https://pytorch-lightning.readthedocs.io/en/stable/common/trainer.html#trainer-flags , and
      https://pytorch-lightning.readthedocs.io/en/stable/accelerators/gpu_basic.html#train-on-multiple-gpus
  
      With parameter ``"callbacks"`` you can add custom or PyTorch-Lightning built-in callbacks to Darts'
      :class:`TorchForecastingModel`. Below is an example for adding EarlyStopping to the training process.
      The model will stop training early if the validation loss `val_loss` does not improve beyond
      specifications. For more information on callbacks, visit:
      `PyTorch Lightning Callbacks
      <https://pytorch-lightning.readthedocs.io/en/stable/extensions/callbacks.html>`_
  
      .. highlight:: python
      .. code-block:: python
  
          from pytorch_lightning.callbacks.early_stopping import EarlyStopping
  
          # stop training when validation loss does not decrease more than 0.05 (`min_delta`) over
          # a period of 5 epochs (`patience`)
          my_stopper = EarlyStopping(
              monitor="val_loss",
              patience=5,
              min_delta=0.05,
              mode='min',
          )
  
          pl_trainer_kwargs={"callbacks": [my_stopper]}
      ..
  
      Note that you can also use a custom PyTorch Lightning Trainer for training and prediction with optional
      parameter ``trainer`` in :func:`fit()` and :func:`predict()`.
  show_warnings
      whether to show warnings raised from PyTorch Lightning. Useful to detect potential issues of
      your forecasting use case. Default: ``False``.
  
  References
  ----------
  .. [1] C. Challu et al. "N-HiTS: Neural Hierarchical Interpolation for Time Series Forecasting",
         https://arxiv.org/abs/2201.12886
  .. [2] T. Kim et al. "Reversible Instance Normalization for Accurate Time-Series Forecasting against
          Distribution Shift", https://openreview.net/forum?id=cGDAkQo1C0p
  
  Examples
  --------
  >>> from darts.datasets import WeatherDataset
  >>> from darts.models import NHiTSModel
  >>> series = WeatherDataset().load()
  >>> # predicting atmospheric pressure
  >>> target = series['p (mbar)'][:100]
  >>> # optionally, use past observed rainfall (pretending to be unknown beyond index 100)
  >>> past_cov = series['rain (mm)'][:100]
  >>> # increasing the number of blocks
  >>> model = NHiTSModel(
  >>>     input_chunk_length=6,
  >>>     output_chunk_length=6,
  >>>     num_blocks=2,
  >>>     n_epochs=5,
  >>> )
  >>> model.fit(target, past_covariates=past_cov)
  >>> pred = model.predict(6)
  >>> pred.values()
  array([[958.2354389 ],
         [939.23201079],
         [987.51425784],
         [919.41209025],
         [925.09583093],
         [938.95625528]])
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code: def __init__(
      self,
      input_chunk_length: int,
      output_chunk_length: int,
      num_stacks: int = 3,
      num_blocks: int = 1,
      num_layers: int = 2,
      layer_widths: Union[int, List[int]] = 512,
      pooling_kernel_sizes: Optional[Tuple[Tuple[int]]] = None,
      n_freq_downsample: Optional[Tuple[Tuple[int]]] = None,
      dropout: float = 0.1,
      activation: str = "ReLU",
      MaxPool1d: bool = True,
      **kwargs,
  ):
      """An implementation of the N-HiTS model, as presented in [1]_.
  
      N-HiTS is similar to N-BEATS (implemented in :class:`NBEATSModel`),
      but attempts to provide better performance at lower computational cost by introducing
      multi-rate sampling of the inputs and multi-scale interpolation of the outputs.
  
      Similar to :class:`NBEATSModel`, in addition to the univariate version presented in the paper,
      this implementation also supports multivariate series (and covariates) by flattening the model inputs
      to a 1-D series and reshaping the outputs to a tensor of appropriate dimensions. Furthermore, it also
      supports producing probabilistic forecasts (by specifying a `likelihood` parameter).
  
      This model supports past covariates (known for `input_chunk_length` points before prediction time).
  
      The multi-rate sampling is done via MaxPooling, which is controlled by ``pooling_kernel_sizes``.
      This parameter can be a tuple of tuples, of size (num_stacks x num_blocks), specifying the kernel
      size for each block in each stack. If left to ``None``, some default values will be used based on
      ``input_chunk_length``.
      Similarly, the multi-scale interpolation is controlled by ``n_freq_downsample``, which gives the
      downsampling factors to be used in each block of each stack. If left to ``None``, some default
      values will be used based on the ``output_chunk_length``.
  
      Parameters
      ----------
      input_chunk_length
          Number of time steps in the past to take as a model input (per chunk). Applies to the target
          series, and past and/or future covariates (if the model supports it).
      output_chunk_length
          Number of time steps predicted at once (per chunk) by the internal model. Also, the number of future values
          from future covariates to use as a model input (if the model supports future covariates). It is not the same
          as forecast horizon `n` used in `predict()`, which is the desired number of prediction points generated
          using either a one-shot- or auto-regressive forecast. Setting `n <= output_chunk_length` prevents
          auto-regression. This is useful when the covariates don't extend far enough into the future, or to prohibit
          the model from using future values of past and / or future covariates for prediction (depending on the
          model's covariate support).
      num_stacks
          The number of stacks that make up the whole model.
      num_blocks
          The number of blocks making up every stack.
      num_layers
          The number of fully connected layers preceding the final forking layers in each block of every stack.
      layer_widths
          Determines the number of neurons that make up each fully connected layer in each block of every stack.
          If a list is passed, it must have a length equal to `num_stacks` and every entry in that list corresponds
          to the layer width of the corresponding stack. If an integer is passed, every stack will have blocks
          with FC layers of the same width.
      pooling_kernel_sizes
          If set, this parameter must be a tuple of tuples, of size (num_stacks x num_blocks), specifying the kernel
          size for each block in each stack used for the input pooling layer.
          If left to ``None``, some default values will be used based on ``input_chunk_length``.
      n_freq_downsample
          If set, this parameter must be a tuple of tuples, of size (num_stacks x num_blocks), specifying the
          downsampling factors before interpolation, for each block in each stack.
          If left to ``None``, some default values will be used based on ``output_chunk_length``.
      dropout
          The dropout probability to be used in fully connected layers. This is compatible with Monte Carlo dropout
          at inference time for model uncertainty estimation (enabled with ``mc_dropout=True`` at
          prediction time).
      activation
          The activation function of encoder/decoder intermediate layer (default='ReLU').
          Supported activations: ['ReLU','RReLU', 'PReLU', 'Softplus', 'Tanh', 'SELU', 'LeakyReLU',  'Sigmoid']
      MaxPool1d
          Use MaxPool1d pooling. False uses AvgPool1d
      **kwargs
          Optional arguments to initialize the pytorch_lightning.Module, pytorch_lightning.Trainer, and
          Darts' :class:`TorchForecastingModel`.
  
      loss_fn
          PyTorch loss function used for training.
          This parameter will be ignored for probabilistic models if the ``likelihood`` parameter is specified.
          Default: ``torch.nn.MSELoss()``.
      likelihood
          One of Darts' :meth:`Likelihood <darts.utils.likelihood_models.Likelihood>` models to be used for
          probabilistic forecasts. Default: ``None``.
      torch_metrics
          A torch metric or a ``MetricCollection`` used for evaluation. A full list of available metrics can be found
          at https://torchmetrics.readthedocs.io/en/latest/. Default: ``None``.
      optimizer_cls
          The PyTorch optimizer class to be used. Default: ``torch.optim.Adam``.
      optimizer_kwargs
          Optionally, some keyword arguments for the PyTorch optimizer (e.g., ``{'lr': 1e-3}``
          for specifying a learning rate). Otherwise, the default values of the selected ``optimizer_cls``
          will be used. Default: ``None``.
      lr_scheduler_cls
          Optionally, the PyTorch learning rate scheduler class to be used. Specifying ``None`` corresponds
          to using a constant learning rate. Default: ``None``.
      lr_scheduler_kwargs
          Optionally, some keyword arguments for the PyTorch learning rate scheduler. Default: ``None``.
      use_reversible_instance_norm
          Whether to use reversible instance normalization `RINorm` against distribution shift as shown in [2]_.
          It is only applied to the features of the target series and not the covariates.
      batch_size
          Number of time series (input and output sequences) used in each training pass. Default: ``32``.
      n_epochs
          Number of epochs over which to train the model. Default: ``100``.
      model_name
          Name of the model. Used for creating checkpoints and saving tensorboard data. If not specified,
          defaults to the following string ``"YYYY-mm-dd_HH_MM_SS_torch_model_run_PID"``, where the initial part
          of the name is formatted with the local date and time, while PID is the processed ID (preventing models
          spawned at the same time by different processes to share the same model_name). E.g.,
          ``"2021-06-14_09_53_32_torch_model_run_44607"``.
      work_dir
          Path of the working directory, where to save checkpoints and Tensorboard summaries.
          Default: current working directory.
      log_tensorboard
          If set, use Tensorboard to log the different parameters. The logs will be located in:
          ``"{work_dir}/darts_logs/{model_name}/logs/"``. Default: ``False``.
      nr_epochs_val_period
          Number of epochs to wait before evaluating the validation loss (if a validation
          ``TimeSeries`` is passed to the :func:`fit()` method). Default: ``1``.
      force_reset
          If set to ``True``, any previously-existing model with the same name will be reset (all checkpoints will
          be discarded). Default: ``False``.
      save_checkpoints
          Whether to automatically save the untrained model and checkpoints from training.
          To load the model from checkpoint, call :func:`MyModelClass.load_from_checkpoint()`, where
          :class:`MyModelClass` is the :class:`TorchForecastingModel` class that was used (such as :class:`TFTModel`,
          :class:`NBEATSModel`, etc.). If set to ``False``, the model can still be manually saved using
          :func:`save()` and loaded using :func:`load()`. Default: ``False``.
      add_encoders
          A large number of past and future covariates can be automatically generated with `add_encoders`.
          This can be done by adding multiple pre-defined index encoders and/or custom user-made functions that
          will be used as index encoders. Additionally, a transformer such as Darts' :class:`Scaler` can be added to
          transform the generated covariates. This happens all under one hood and only needs to be specified at
          model creation.
          Read :meth:`SequentialEncoder <darts.dataprocessing.encoders.SequentialEncoder>` to find out more about
          ``add_encoders``. Default: ``None``. An example showing some of ``add_encoders`` features:
  
          .. highlight:: python
          .. code-block:: python
  
              def encode_year(idx):
                  return (idx.year - 1950) / 50
  
              add_encoders={
                  'cyclic': {'future': ['month']},
                  'datetime_attribute': {'future': ['hour', 'dayofweek']},
                  'position': {'past': ['relative'], 'future': ['relative']},
                  'custom': {'past': [encode_year]},
                  'transformer': Scaler(),
                  'tz': 'CET'
              }
          ..
      random_state
          Control the randomness of the weights initialization. Check this
          `link <https://scikit-learn.org/stable/glossary.html#term-random_state>`_ for more details.
          Default: ``None``.
      pl_trainer_kwargs
          By default :class:`TorchForecastingModel` creates a PyTorch Lightning Trainer with several useful presets
          that performs the training, validation and prediction processes. These presets include automatic
          checkpointing, tensorboard logging, setting the torch device and more.
          With ``pl_trainer_kwargs`` you can add additional kwargs to instantiate the PyTorch Lightning trainer
          object. Check the `PL Trainer documentation
          <https://pytorch-lightning.readthedocs.io/en/stable/common/trainer.html>`_ for more information about the
          supported kwargs. Default: ``None``.
          Running on GPU(s) is also possible using ``pl_trainer_kwargs`` by specifying keys ``"accelerator",
          "devices", and "auto_select_gpus"``. Some examples for setting the devices inside the ``pl_trainer_kwargs``
          dict:
  
          - ``{"accelerator": "cpu"}`` for CPU,
          - ``{"accelerator": "gpu", "devices": [i]}`` to use only GPU ``i`` (``i`` must be an integer),
          - ``{"accelerator": "gpu", "devices": -1, "auto_select_gpus": True}`` to use all available GPUS.
  
          For more info, see here:
          https://pytorch-lightning.readthedocs.io/en/stable/common/trainer.html#trainer-flags , and
          https://pytorch-lightning.readthedocs.io/en/stable/accelerators/gpu_basic.html#train-on-multiple-gpus
  
          With parameter ``"callbacks"`` you can add custom or PyTorch-Lightning built-in callbacks to Darts'
          :class:`TorchForecastingModel`. Below is an example for adding EarlyStopping to the training process.
          The model will stop training early if the validation loss `val_loss` does not improve beyond
          specifications. For more information on callbacks, visit:
          `PyTorch Lightning Callbacks
          <https://pytorch-lightning.readthedocs.io/en/stable/extensions/callbacks.html>`_
  
          .. highlight:: python
          .. code-block:: python
  
              from pytorch_lightning.callbacks.early_stopping import EarlyStopping
  
              # stop training when validation loss does not decrease more than 0.05 (`min_delta`) over
              # a period of 5 epochs (`patience`)
              my_stopper = EarlyStopping(
                  monitor="val_loss",
                  patience=5,
                  min_delta=0.05,
                  mode='min',
              )
  
              pl_trainer_kwargs={"callbacks": [my_stopper]}
          ..
  
          Note that you can also use a custom PyTorch Lightning Trainer for training and prediction with optional
          parameter ``trainer`` in :func:`fit()` and :func:`predict()`.
      show_warnings
          whether to show warnings raised from PyTorch Lightning. Useful to detect potential issues of
          your forecasting use case. Default: ``False``.
  
      References
      ----------
      .. [1] C. Challu et al. "N-HiTS: Neural Hierarchical Interpolation for Time Series Forecasting",
             https://arxiv.org/abs/2201.12886
      .. [2] T. Kim et al. "Reversible Instance Normalization for Accurate Time-Series Forecasting against
              Distribution Shift", https://openreview.net/forum?id=cGDAkQo1C0p
  
      Examples
      --------
      >>> from darts.datasets import WeatherDataset
      >>> from darts.models import NHiTSModel
      >>> series = WeatherDataset().load()
      >>> # predicting atmospheric pressure
      >>> target = series['p (mbar)'][:100]
      >>> # optionally, use past observed rainfall (pretending to be unknown beyond index 100)
      >>> past_cov = series['rain (mm)'][:100]
      >>> # increasing the number of blocks
      >>> model = NHiTSModel(
      >>>     input_chunk_length=6,
      >>>     output_chunk_length=6,
      >>>     num_blocks=2,
      >>>     n_epochs=5,
      >>> )
      >>> model.fit(target, past_covariates=past_cov)
      >>> pred = model.predict(6)
      >>> pred.values()
      array([[958.2354389 ],
             [939.23201079],
             [987.51425784],
             [919.41209025],
             [925.09583093],
             [938.95625528]])
      """
      super().__init__(
          **self._extract_torch_model_params(**self.model_params)
      )
  
      # extract pytorch lightning module kwargs
      self.pl_module_params = self._extract_pl_module_params(
          **self.model_params
      )
  
      raise_if_not(
          isinstance(layer_widths, int)
          or len(layer_widths) == num_stacks,
          "Please pass an integer or a list of integers with length `num_stacks`"
          "as value for the `layer_widths` argument.",
          logger,
      )
  
      self.num_stacks = num_stacks
      self.num_blocks = num_blocks
      self.num_layers = num_layers
      self.layer_widths = layer_widths
      self.activation = activation
      self.MaxPool1d = MaxPool1d
  
      # Currently batch norm is not an option as it seems to perform badly
      self.batch_norm = False
  
      self.dropout = dropout
  
      # Check pooling and downsampling numbers or compute good defaults
      sizes = self._prepare_pooling_downsampling(
          pooling_kernel_sizes,
          n_freq_downsample,
          self.input_chunk_length,
          self.output_chunk_length,
          num_blocks,
          num_stacks,
      )
      self.pooling_kernel_sizes, self.n_freq_downsample = sizes
  
      if isinstance(layer_widths, int):
          self.layer_widths = [layer_widths] * self.num_stacks
  
imports: from typing import List, Optional, Tuple, Union
  import numpy
  import torch
  import torch.nn
  import torch.nn.functional
  from darts.logging import get_logger, raise_if_not
  from darts.models.forecasting.pl_forecasting_module import PLPastCovariatesModule, io_processor
  from darts.models.forecasting.torch_forecasting_model import PastCovariatesTorchModel
  from darts.utils.torch import MonteCarloDropout
label_desc: Use image node_id 1 to create a new NHiTSModel object from inherited base classes: PastCovariatesTorchModel with example: obj = NHiTSModel(input_chunk_length, output_chunk_length, num_stacks, num_blocks, num_layers, layer_widths, pooling_kernel_sizes, n_freq_downsample, dropout, activation, MaxPool1d)