class_docstr: MLP
  
  Simple Multi Layer Perceptron architecture (MLP).
  This deep neural network has constant units through its layers, each with
  ReLU non-linearities, it is trained using ADAM stochastic gradient descent.
  The network accepts static, historic and future exogenous data, flattens
  the inputs and learns fully connected relationships against the target variable.
  
  **Parameters:**<br>
  `h`: int, forecast horizon.<br>
  `input_size`: int, considered autorregresive inputs (lags), y=[1,2,3,4] input_size=2 -> lags=[1,2].<br>
  `stat_exog_list`: str list, static exogenous columns.<br>
  `hist_exog_list`: str list, historic exogenous columns.<br>
  `futr_exog_list`: str list, future exogenous columns.<br>
  `exclude_insample_y`: bool=False, the model skips the autoregressive features y[t-input_size:t] if True.<br>
  `n_layers`: int, number of layers for the MLP.<br>
  `hidden_size`: int, number of units for each layer of the MLP.<br>
  `loss`: PyTorch module, instantiated train loss class from [losses collection](https://nixtla.github.io/neuralforecast/losses.pytorch.html).<br>
  `valid_loss`: PyTorch module=`loss`, instantiated valid loss class from [losses collection](https://nixtla.github.io/neuralforecast/losses.pytorch.html).<br>
  `max_steps`: int=1000, maximum number of training steps.<br>
  `learning_rate`: float=1e-3, Learning rate between (0, 1).<br>
  `num_lr_decays`: int=-1, Number of learning rate decays, evenly distributed across max_steps.<br>
  `early_stop_patience_steps`: int=-1, Number of validation iterations before early stopping.<br>
  `val_check_steps`: int=100, Number of training steps between every validation loss check.<br>
  `batch_size`: int=32, number of different series in each batch.<br>
  `valid_batch_size`: int=None, number of different series in each validation and test batch, if None uses batch_size.<br>
  `windows_batch_size`: int=1024, number of windows to sample in each training batch, default uses all.<br>
  `inference_windows_batch_size`: int=-1, number of windows to sample in each inference batch, -1 uses all.<br>
  `start_padding_enabled`: bool=False, if True, the model will pad the time series with zeros at the beginning, by input size.<br>
  `step_size`: int=1, step size between each window of temporal data.<br>
  `scaler_type`: str='identity', type of scaler for temporal inputs normalization see [temporal scalers](https://nixtla.github.io/neuralforecast/common.scalers.html).<br>
  `random_seed`: int=1, random_seed for pytorch initializer and numpy generators.<br>
  `num_workers_loader`: int=os.cpu_count(), workers to be used by `TimeSeriesDataLoader`.<br>
  `drop_last_loader`: bool=False, if True `TimeSeriesDataLoader` drops last non-full batch.<br>
  `alias`: str, optional,  Custom name of the model.<br>
  `**trainer_kwargs`: int,  keyword trainer arguments inherited from [PyTorch Lighning's trainer](https://pytorch-lightning.readthedocs.io/en/stable/api/pytorch_lightning.trainer.trainer.Trainer.html?highlight=trainer).<br>
code: def __init__(
      self,
      h,
      input_size,
      futr_exog_list=None,
      hist_exog_list=None,
      stat_exog_list=None,
      exclude_insample_y=False,
      num_layers=2,
      hidden_size=1024,
      loss=MAE(),
      valid_loss=None,
      max_steps: int = 1000,
      learning_rate: float = 1e-3,
      num_lr_decays: int = -1,
      early_stop_patience_steps: int = -1,
      val_check_steps: int = 100,
      batch_size: int = 32,
      valid_batch_size: Optional[int] = None,
      windows_batch_size=1024,
      inference_windows_batch_size=-1,
      start_padding_enabled=False,
      step_size: int = 1,
      scaler_type: str = "identity",
      random_seed: int = 1,
      num_workers_loader: int = 0,
      drop_last_loader: bool = False,
      **trainer_kwargs,
  ):
      # Inherit BaseWindows class
      super(MLP, self).__init__(
          h=h,
          input_size=input_size,
          futr_exog_list=futr_exog_list,
          hist_exog_list=hist_exog_list,
          stat_exog_list=stat_exog_list,
          exclude_insample_y=exclude_insample_y,
          loss=loss,
          valid_loss=valid_loss,
          max_steps=max_steps,
          learning_rate=learning_rate,
          num_lr_decays=num_lr_decays,
          early_stop_patience_steps=early_stop_patience_steps,
          val_check_steps=val_check_steps,
          batch_size=batch_size,
          valid_batch_size=valid_batch_size,
          windows_batch_size=windows_batch_size,
          inference_windows_batch_size=inference_windows_batch_size,
          start_padding_enabled=start_padding_enabled,
          step_size=step_size,
          scaler_type=scaler_type,
          num_workers_loader=num_workers_loader,
          drop_last_loader=drop_last_loader,
          random_seed=random_seed,
          **trainer_kwargs,
      )
  
      # Architecture
      self.num_layers = num_layers
      self.hidden_size = hidden_size
  
      self.futr_input_size = len(self.futr_exog_list)
      self.hist_input_size = len(self.hist_exog_list)
      self.stat_input_size = len(self.stat_exog_list)
  
      input_size_first_layer = (
          input_size
          + self.hist_input_size * input_size
          + self.futr_input_size * (input_size + h)
          + self.stat_input_size
      )
  
      # MultiLayer Perceptron
      layers = [
          nn.Linear(
              in_features=input_size_first_layer,
              out_features=hidden_size,
          )
      ]
      for i in range(num_layers - 1):
          layers += [
              nn.Linear(
                  in_features=hidden_size, out_features=hidden_size
              )
          ]
      self.mlp = nn.ModuleList(layers)
  
      # Adapter with Loss dependent dimensions
      self.out = nn.Linear(
          in_features=hidden_size,
          out_features=h * self.loss.outputsize_multiplier,
      )
  
file_path: neuralforecast/neuralforecast/models/mlp.py
imports: from typing import Optional
  import torch
  import torch.nn
  from ..losses.pytorch import MAE
  from ..common._base_windows import BaseWindows
label_desc: Use image node_id 1 to create a new MLP object from inherited base classes: BaseWindows with example: obj = MLP(h, input_size, futr_exog_list, hist_exog_list, stat_exog_list, exclude_insample_y, num_layers, hidden_size, loss, valid_loss, max_steps, learning_rate, num_lr_decays, early_stop_patience_steps, val_check_steps, batch_size, valid_batch_size, windows_batch_size, inference_windows_batch_size, start_padding_enabled, step_size, scaler_type, random_seed, num_workers_loader, drop_last_loader)