class_docstr: Abstract base class to implement feed-forward networks for probabilistic
  time series prediction.
  
  This class does not implement hybrid_forward: this is delegated
  to the two subclasses SimpleFeedForwardTrainingNetwork and
  SimpleFeedForwardPredictionNetwork, that define respectively how to
  compute the loss and how to generate predictions.
  
  Parameters
  ----------
  num_hidden_dimensions
      Number of hidden nodes in each layer.
  prediction_length
      Number of time units to predict.
  context_length
      Number of time units that condition the predictions.
  batch_normalization
      Whether to use batch normalization.
  mean_scaling
      Scale the network input by the data mean and the network output by
      its inverse.
  distr_output
      Distribution to fit.
  kwargs
docstr: Given past target values, applies the feed-forward network and maps the
  output to the parameter of probability distribution for future
  observations.
  
  Parameters
  ----------
  F
  past_target
      Tensor containing past target observations.
      Shape: (batch_size, context_length, target_dim).
  
  Returns
  -------
  Tensor
      The parameters of distribution.
  Tensor
      An array containing the location (shift) of the distribution.
  Tensor
      An array containing the scale of the distribution.
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code: def get_distr_args(
      self, F, past_target: Tensor
  ) -> Tuple[Tensor, Tensor, Tensor]:
      """
      Given past target values, applies the feed-forward network and maps the
      output to the parameter of probability distribution for future
      observations.
  
      Parameters
      ----------
      F
      past_target
          Tensor containing past target observations.
          Shape: (batch_size, context_length, target_dim).
  
      Returns
      -------
      Tensor
          The parameters of distribution.
      Tensor
          An array containing the location (shift) of the distribution.
      Tensor
          An array containing the scale of the distribution.
      """
      scaled_target, target_scale = self.scaler(
          past_target,
          F.ones_like(past_target),
      )
      mlp_outputs = self.mlp(scaled_target)
      distr_args = self.distr_args_proj(mlp_outputs)
      scale = target_scale.expand_dims(axis=1)
      loc = F.zeros_like(scale)
      return distr_args, loc, scale
  
imports: from typing import List, Tuple
  import mxnet
  from gluonts.core.component import validated
  from gluonts.mx import Tensor
  from gluonts.mx.block.scaler import MeanScaler, NOPScaler
  from gluonts.mx.distribution import DistributionOutput
  from gluonts.mx.util import weighted_average
label_desc: Use image node_id 2 for calling the SimpleFeedForwardNetworkBase obj's underlying member method code with example usage: obj.get_distr_args(F, past_target) and returns: distr_args,  loc,  scale