class_docstr: Module implementing a feed-forward model for forecasting.
  
  Parameters
  ----------
  prediction_length
      Number of time points to predict.
  context_length
      Number of time steps prior to prediction time that the model.
  hidden_dimensions
      Size of hidden layers in the feed-forward network.
  distr_output
      Distribution to use to evaluate observations and sample predictions.
      Default: ``StudentTOutput()``.
  batch_norm
      Whether to apply batch normalization. Default: ``False``.
class_docstr_tok: ["Module","implementing","a","feed-forward","model","for","forecasting",".","Parameters","--","--","--","--","--","prediction_length","Number","of","time","points","to","predict",".","context_length","Number","of","time","steps","prior","to","prediction","time","that","the","model",".","hidden_dimensions","Size","of","hidden","layers","in","the","feed-forward","network",".","distr_output","Distribution","to","use","to","evaluate","observations","and","sample","predictions",".","Default",":","``","StudentTOutput","(",")","``",".","batch_norm","Whether","to","apply","batch","normalization",".","Default",":","``","False","``","."]
code: def forward(
      self,
      past_target: torch.Tensor,
  ) -> Tuple[Tuple[torch.Tensor, ...], torch.Tensor, torch.Tensor]:
      scale = mean_abs_scaling(past_target)
      scaled_context = past_target / scale
      nn_out = self.nn(scaled_context)
      nn_out_reshaped = nn_out.reshape(
          -1, self.prediction_length, self.hidden_dimensions[-1]
      )
      distr_args = self.args_proj(nn_out_reshaped)
      return distr_args, torch.zeros_like(scale), scale
  
file_path: gluonts/src/gluonts/torch/model/simple_feedforward/module.py
imports: from typing import List, Optional, Tuple
  import torch
  from torch import nn
  from gluonts.core.component import validated
  from gluonts.model import Input, InputSpec
  from gluonts.torch.distributions import Output, StudentTOutput
  from gluonts.torch.util import weighted_average
label_desc: Use image node_id 3 for calling the SimpleFeedForwardModel obj's underlying member method code with example usage: obj.forward(past_target) and returns: distr_args,  torch,  scale