class_docstr: StemGNN
  
  The Spectral Temporal Graph Neural Network (`StemGNN`) is a Graph-based multivariate
  time-series forecasting model. `StemGNN` jointly learns temporal dependencies and
  inter-series correlations in the spectral domain, by combining Graph Fourier Transform (GFT)
  and Discrete Fourier Transform (DFT).
  
  **Parameters:**<br>
  `h`: int, Forecast horizon. <br>
  `input_size`: int, autorregresive inputs size, y=[1,2,3,4] input_size=2 -> y_[t-2:t]=[1,2].<br>
  `n_series`: int, number of time-series.<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>
  `n_stacks`: int=2, number of stacks in the model.<br>
  `multi_layer`: int=5, multiplier for FC hidden size on StemGNN blocks.<br>
  `dropout_rate`: float=0.5, dropout rate.<br>
  `leaky_rate`: float=0.2, alpha for LeakyReLU layer on Latent Correlation layer.<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, number of windows in each batch.<br>
  `step_size`: int=1, step size between each window of temporal data.<br>
  `scaler_type`: str='robust', type of scaler for temporal inputs normalization see [temporal scalers](https://nixtla.github.io/neuralforecast/common.scalers.html).<br>
  `random_seed`: int, 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 graph_fft(self, input, eigenvectors):
      return torch.matmul(eigenvectors, input)
  
imports: import torch
  import torch.nn
  import torch.nn.functional
  from ..losses.pytorch import MAE
  from ..common._base_multivariate import BaseMultivariate
label_desc: Use image node_id 6 for calling the StemGNN obj's underlying member method code with example usage: obj.graph_fft(input, eigenvectors) and returns: torch