code: def transition_coeff(
      self, feature: Tensor  # (batch_size, time_length, 1)
  ) -> Tensor:
      F = getF(feature)
  
      _transition_coeff = (
          (
              F.diag(F.ones(shape=(2,)), k=0)
              + F.diag(F.ones(shape=(1,)), k=1)
          )
          .expand_dims(axis=0)
          .expand_dims(axis=0)
      )
  
      # get the right shape: (batch_size, time_length, latent_dim,
      # latent_dim)
      zeros = _broadcast_param(
          feature.squeeze(axis=2),
          axes=[2, 3],
          sizes=[self.latent_dim(), self.latent_dim()],
      )
  
      return _transition_coeff.broadcast_like(zeros)
  
imports: from typing import List, Sequence, Tuple
  from pandas.tseries.frequencies import to_offset
  from gluonts.core.component import validated
  from gluonts.mx import Tensor
  from gluonts.mx.distribution.distribution import getF
  from gluonts.mx.util import _broadcast_param
  from gluonts.time_feature import Constant, day_of_week_index, hour_of_day_index, minute_of_hour_index, month_of_year_index, TimeFeature, week_of_year_index, norm_freq_str
label_desc: Use image node_id 4 for calling the LevelTrendISSM obj's underlying member method code with example usage: obj.transition_coeff(feature) and returns: _transition_coeff