docstr: Temporal Convolutional Network Model (TCN).
  
  This is an implementation of a dilated TCN used for forecasting, inspired from [1]_.
  
  This model supports past covariates (known for `input_chunk_length` points before prediction time).
  
  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).
  kernel_size
      The size of every kernel in a convolutional layer.
  num_filters
      The number of filters in a convolutional layer of the TCN.
  weight_norm
      Boolean value indicating whether to use weight normalization.
  dilation_base
      The base of the exponent that will determine the dilation on every level.
  num_layers
      The number of convolutional layers.
  dropout
      The dropout rate for every convolutional layer. This is compatible with Monte Carlo dropout
      at inference time for model uncertainty estimation (enabled with ``mc_dropout=True`` at
      prediction time).
  **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:rgs``
      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] https://arxiv.org/abs/1803.01271
  .. [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 TCNModel
  >>> 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]
  >>> # `output_chunk_length` must be strictly smaller than `input_chunk_length`
  >>> model = TCNModel(
  >>>     input_chunk_length=12,
  >>>     output_chunk_length=6,
  >>>     n_epochs=20,
  >>> )
  >>> model.fit(target, past_covariates=past_cov)
  >>> pred = model.predict(6)
  >>> pred.values()
  array([[-80.48476824],
         [-80.47896667],
         [-41.77135603],
         [-41.76158729],
         [-41.76854107],
         [-41.78166819]])
  
  .. note::
      `DeepTCN example notebook <https://unit8co.github.io/darts/examples/09-DeepTCN-examples.html>`_ presents
      techniques that can be used to improve the forecasts quality compared to this simple usage example.
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code: def __init__(
      self,
      input_chunk_length: int,
      output_chunk_length: int,
      kernel_size: int = 3,
      num_filters: int = 3,
      num_layers: Optional[int] = None,
      dilation_base: int = 2,
      weight_norm: bool = False,
      dropout: float = 0.2,
      **kwargs,
  ):
      """Temporal Convolutional Network Model (TCN).
  
      This is an implementation of a dilated TCN used for forecasting, inspired from [1]_.
  
      This model supports past covariates (known for `input_chunk_length` points before prediction time).
  
      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).
      kernel_size
          The size of every kernel in a convolutional layer.
      num_filters
          The number of filters in a convolutional layer of the TCN.
      weight_norm
          Boolean value indicating whether to use weight normalization.
      dilation_base
          The base of the exponent that will determine the dilation on every level.
      num_layers
          The number of convolutional layers.
      dropout
          The dropout rate for every convolutional layer. This is compatible with Monte Carlo dropout
          at inference time for model uncertainty estimation (enabled with ``mc_dropout=True`` at
          prediction time).
      **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:rgs``
          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] https://arxiv.org/abs/1803.01271
      .. [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 TCNModel
      >>> 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]
      >>> # `output_chunk_length` must be strictly smaller than `input_chunk_length`
      >>> model = TCNModel(
      >>>     input_chunk_length=12,
      >>>     output_chunk_length=6,
      >>>     n_epochs=20,
      >>> )
      >>> model.fit(target, past_covariates=past_cov)
      >>> pred = model.predict(6)
      >>> pred.values()
      array([[-80.48476824],
             [-80.47896667],
             [-41.77135603],
             [-41.76158729],
             [-41.76854107],
             [-41.78166819]])
  
      .. note::
          `DeepTCN example notebook <https://unit8co.github.io/darts/examples/09-DeepTCN-examples.html>`_ presents
          techniques that can be used to improve the forecasts quality compared to this simple usage example.
      """
  
      raise_if_not(
          kernel_size < input_chunk_length,
          "The kernel size must be strictly smaller than the input length.",
          logger,
      )
      raise_if_not(
          output_chunk_length < input_chunk_length,
          "The output length must be strictly smaller than the input length",
          logger,
      )
  
      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
      )
  
      self.kernel_size = kernel_size
      self.num_filters = num_filters
      self.num_layers = num_layers
      self.dilation_base = dilation_base
      self.dropout = dropout
      self.weight_norm = weight_norm
  
imports: import math
  from typing import Optional, Sequence, Tuple
  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.timeseries import TimeSeries
  from darts.utils.data import PastCovariatesShiftedDataset
  from darts.utils.torch import MonteCarloDropout
label_desc: Use image node_id 1 to create a new TCNModel object from inherited base classes: PastCovariatesTorchModel with example: obj = TCNModel(input_chunk_length, output_chunk_length, kernel_size, num_filters, num_layers, dilation_base, weight_norm, dropout)