docstr: Parameters
  ----------
  F
  past_target
      Tensor with past observations.
      Shape: (batch_size, context_length, target_dim).
  past_observed_values
      Tensor with past observed values.
      Shape: (batch_size, context_length, target_dim).
  future_target
      Not used.
  future_observed_values
      Not used.
  
  Returns
  -------
  Tensor
      Prediction sample. Shape: (batch_size, 1, prediction_length).
docstr_tok: ["Parameters","--","--","--","--","--","F","past_target","Tensor","with","past","observations",".","Shape",":","(","batch_size",",","context_length",",","target_dim",")",".","past_observed_values","Tensor","with","past","observed","values",".","Shape",":","(","batch_size",",","context_length",",","target_dim",")",".","future_target","Not","used",".","future_observed_values","Not","used",".","Returns","--","--","--","-","Tensor","Prediction","sample",".","Shape",":","(","batch_size",",","1",",","prediction_length",")","."]
code: def hybrid_forward(
      self,
      F,
      past_target: Tensor,
      past_observed_values: Tensor,
      future_target: Optional[Tensor] = None,
      future_observed_values: Optional[Tensor] = None,
  ) -> Tensor:
      """
  
      Parameters
      ----------
      F
      past_target
          Tensor with past observations.
          Shape: (batch_size, context_length, target_dim).
      past_observed_values
          Tensor with past observed values.
          Shape: (batch_size, context_length, target_dim).
      future_target
          Not used.
      future_observed_values
          Not used.
  
      Returns
      -------
      Tensor
          Prediction sample. Shape: (batch_size, 1, prediction_length).
      """
      forecasts = super().hybrid_forward(
          F,
          past_target=past_target,
          past_observed_values=past_observed_values,
          future_target=None,
          future_observed_values=None,
      )
  
      # dimension collapsed previously because we only have one sample each:
      forecasts = F.expand_dims(forecasts, axis=1)
  
      # (batch_size, 1, prediction_length)
      return forecasts
  
imports: from typing import List, Optional
  import mxnet
  import numpy
  from gluonts.core.component import validated
  from gluonts.mx import Tensor
  from gluonts.mx.block.scaler import MeanScaler, NOPScaler
  from gluonts.time_feature import get_seasonality
label_desc: Use image node_id 2 for calling the NBEATSPredictionNetwork obj's underlying member method code with example usage: obj.hybrid_forward(F, past_target, past_observed_values, future_target, future_observed_values) and returns: forecasts