docstr: Computes the parameters of distribution for future data given the past,
  and draws samples from it.
  
  Parameters
  ----------
  F
  past_target
      Tensor with past 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 hybrid_forward(self, F, past_target: Tensor) -> Tensor:
      """
      Computes the parameters of distribution for future data given the past,
      and draws samples from it.
  
      Parameters
      ----------
      F
      past_target
          Tensor with past 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.
      """
      distr_args, loc, scale = self.get_distr_args(F, past_target)
      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 SimpleFeedForwardDistributionNetwork obj's underlying member method code with example usage: obj.hybrid_forward(F, past_target) and returns: distr_args,  loc,  scale