class_docstr: The NBEATS Block as described in the paper:
  https://arxiv.org/abs/1905.10437. This is the Trend block variant.
  
  Parameters
  ----------
  width
      Width of the fully connected layers with ReLu activation.
  num_block_layers
      Number of fully connected layers with ReLu activation.
  expansion_coefficient_length
      The length of the number of expansion coefficients.
      This corresponds to degree of the polynomial basis as follows:
          expansion_coefficient_length-1
  prediction_length
      Length of the prediction. Also known as 'horizon'.
  context_length
      Number of time units that condition the predictions
      Also known as 'lookback period'.
  has_backcast
      Only the last block of the network doesn't.
  kwargs
      Arguments passed to 'HybridBlock'.
code: def initialize_basis(self, F):
      # these are essentially constant matrices of type F that
      # define the basis for the trend model
      if self.has_backcast:
          self.backcast_basis = trend_model(
              F,
              num_coefficients=self.expansion_coefficient_length,
              context_length=self.context_length,
              prediction_length=self.prediction_length,
              is_forecast=False,
          )
      self.forecast_basis = trend_model(
          F,
          num_coefficients=self.expansion_coefficient_length,
          context_length=self.context_length,
          prediction_length=self.prediction_length,
          is_forecast=True,
      )
  
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 NBEATSTrendBlock obj's underlying member method code with example usage: obj.initialize_basis(F) without return types