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 forward(self, windows_batch):
      # Parse windows_batch
      insample_y = windows_batch["insample_y"]
      futr_exog = windows_batch["futr_exog"]
      hist_exog = windows_batch["hist_exog"]
      stat_exog = windows_batch["stat_exog"]
  
      # Flatten MLP inputs [B, L+H, C] -> [B, (L+H)*C]
      # Contatenate [ Y_t, | X_{t-L},..., X_{t} | F_{t-L},..., F_{t+H} | S ]
      batch_size = len(insample_y)
      if self.hist_input_size > 0:
          insample_y = torch.cat(
              (insample_y, hist_exog.reshape(batch_size, -1)), dim=1
          )
  
      if self.futr_input_size > 0:
          insample_y = torch.cat(
              (insample_y, futr_exog.reshape(batch_size, -1)), dim=1
          )
  
      if self.stat_input_size > 0:
          insample_y = torch.cat(
              (insample_y, stat_exog.reshape(batch_size, -1)), dim=1
          )
  
      y_pred = insample_y.clone()
      for layer in self.mlp:
          y_pred = torch.relu(layer(y_pred))
      y_pred = self.out(y_pred)
  
      y_pred = y_pred.reshape(
          batch_size, self.h, self.loss.outputsize_multiplier
      )
      y_pred = self.loss.domain_map(y_pred)
      return y_pred
  
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 2 for calling the MLP obj's underlying member method code with example usage: obj.forward(windows_batch) and returns: y_pred