code: def unroll(
      self,
      lags: torch.Tensor,
      scale: torch.Tensor,
      time_feat: torch.Tensor,
      feat_static_cat: torch.Tensor,
      feat_static_real: torch.Tensor,
      unroll_length: int,
      begin_state: Optional[
          Union[List[torch.Tensor], torch.Tensor]
      ] = None,
  ) -> Tuple[
      torch.Tensor,
      Union[List[torch.Tensor], torch.Tensor],
      torch.Tensor,
      torch.Tensor,
  ]:
      # (batch_size, sub_seq_len, target_dim, num_lags)
      lags_scaled = lags / scale.unsqueeze(-1)
  
      # assert_shape(
      #     lags_scaled, (-1, unroll_length, self.target_dim, len(self.lags_seq)),
      # )
      input_lags = lags_scaled.reshape(
          (-1, unroll_length, len(self.lags_seq) * self.target_dim)
      )
  
      # (batch_size, target_dim, embed_dim)
      embedded_cat = self.embed(feat_static_cat)
      # assert_shape(index_embeddings, (-1, self.target_dim, self.embed_dim))
  
      static_feat = torch.cat(
          (
              embedded_cat,
              feat_static_real,
              scale.log()
              if len(self.target_shape) == 0
              else scale.squeeze(1).log(),
          ),
          dim=1,
      )
  
      # (batch_size, seq_len, embed_dim)
      repeated_static_feat = static_feat.unsqueeze(1).expand(
          -1, unroll_length, -1
      )
  
      # (batch_size, sub_seq_len, input_dim)
  
      inputs = torch.cat(
          (input_lags, repeated_static_feat, time_feat), dim=-1
      )
  
      # unroll encoder
  
      outputs, state = self.rnn(inputs, begin_state)
  
      # assert_shape(outputs, (-1, unroll_length, self.num_cells))
      # for s in state:
      #     assert_shape(s, (-1, self.num_cells))
  
      # assert_shape(
      #     lags_scaled, (-1, unroll_length, self.target_dim, len(self.lags_seq)),
      # )
  
      return outputs, state, lags_scaled, inputs
  
imports: from turtle import distance
  from typing import List, Optional, Tuple, Union
  from matplotlib.pyplot import sca
  import torch
  import torch.nn
  from gluonts.core.component import validated
  from gluonts.torch.modules.distribution_output import DistributionOutput
  from pts.model import weighted_average
  from pts.modules import MeanScaler, NOPScaler, FeatureEmbedder
  from torch.distributions import AffineTransform, Beta, Distribution, Gamma, NegativeBinomial, Normal, Poisson, StudentT, TransformedDistribution
label_desc: Use image node_id 3 for calling the DeepVARTrainingNetwork obj's underlying member method code with example usage: obj.unroll(lags, scale, time_feat, feat_static_cat, feat_static_real, unroll_length, begin_state) and returns: outputs,  state,  lags_scaled,  inputs