docstr: Computes a probability 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
      Prediction sample. Shape: (batch_size, samples, prediction_length).
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code: def hybrid_forward(self, F, past_target: Tensor) -> Tensor:
      """
      Computes a probability 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
          Prediction sample. Shape: (batch_size, samples, prediction_length).
      """
  
      distr_args, loc, scale = self.get_distr_args(F, past_target)
      distr = self.distr_output.distribution(
          distr_args, loc=loc, scale=scale
      )
  
      # (num_samples, batch_size, prediction_length)
      samples = distr.sample(self.num_parallel_samples)
  
      # (batch_size, num_samples, prediction_length)
      return samples.swapaxes(0, 1)
  
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 SimpleFeedForwardSamplingNetwork obj's underlying member method code with example usage: obj.hybrid_forward(F, past_target) and returns: samples