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 loss(
      self,
      past_target: torch.Tensor,
      future_target: torch.Tensor,
      future_observed_values: torch.Tensor,
  ) -> torch.Tensor:
      distr_args, loc, scale = self(past_target=past_target)
      loss = self.distr_output.loss(
          target=future_target,
          distr_args=distr_args,
          loc=loc,
          scale=scale,
      )
      return weighted_average(
          loss, weights=future_observed_values, dim=-1
      )
  
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 4 for calling the SimpleFeedForwardModel obj's underlying member method code with example usage: obj.loss(past_target, future_target, future_observed_values) and returns: weighted_average