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``.
code: def __init__(
      self,
      prediction_length: int,
      context_length: int,
      hidden_dimensions: Optional[List[int]] = None,
      distr_output: Output = StudentTOutput(),
      batch_norm: bool = False,
  ) -> None:
      super().__init__()
  
      assert prediction_length > 0
      assert context_length > 0
      assert hidden_dimensions is None or len(hidden_dimensions) > 0
  
      self.prediction_length = prediction_length
      self.context_length = context_length
      self.hidden_dimensions = (
          hidden_dimensions
          if hidden_dimensions is not None
          else [20, 20]
      )
      self.distr_output = distr_output
      self.batch_norm = batch_norm
  
      dimensions = [context_length] + self.hidden_dimensions[:-1]
  
      modules = []
      for in_size, out_size in zip(dimensions[:-1], dimensions[1:]):
          modules += [make_linear_layer(in_size, out_size), nn.ReLU()]
          if batch_norm:
              modules.append(nn.BatchNorm1d(out_size))
      modules.append(
          make_linear_layer(
              dimensions[-1],
              prediction_length * self.hidden_dimensions[-1],
          )
      )
  
      self.nn = nn.Sequential(*modules)
      self.args_proj = self.distr_output.get_args_proj(
          self.hidden_dimensions[-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 1 to create a new SimpleFeedForwardModel object from inherited base classes: nn with example: obj = SimpleFeedForwardModel(prediction_length, context_length, hidden_dimensions, distr_output, batch_norm)