docstr: Returns the LSTNet predictions. All tensors have NCT layout.
  
  Parameters
  ----------
  F
  past_target
      Tensor of shape (batch_size, num_series, context_length)
  past_observed_values
      Tensor of shape (batch_size, num_series, context_length)
  
  Returns
  -------
  Tensor
      Predicted samples of shape (batch_size, num_samples,
      prediction_length, num_series)
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code: def hybrid_forward(
      self, F, past_target: Tensor, past_observed_values: Tensor
  ) -> Tensor:
      """
      Returns the LSTNet predictions. All tensors have NCT layout.
  
      Parameters
      ----------
      F
      past_target
          Tensor of shape (batch_size, num_series, context_length)
      past_observed_values
          Tensor of shape (batch_size, num_series, context_length)
  
      Returns
      -------
      Tensor
          Predicted samples of shape (batch_size, num_samples,
          prediction_length, num_series)
      """
  
      ret, scale = super().hybrid_forward(
          F, past_target, past_observed_values
      )
      ret = F.swapaxes(F.broadcast_mul(ret, scale), 1, 2)
      return ret.expand_dims(axis=1)
  
imports: from typing import Optional, Type
  import mxnet
  from mxnet.gluon import loss, nn
  from gluonts.core.component import validated
  from gluonts.mx import Tensor
  from gluonts.mx.block.scaler import MeanScaler, NOPScaler
label_desc: Use image node_id 1 for calling the LSTNetPredict obj's underlying member method code with example usage: obj.hybrid_forward(F, past_target, past_observed_values) and returns: ret