code: def unroll(
      self,
      lags: torch.Tensor,
      scale: torch.Tensor,
      time_feat: torch.Tensor,
      target_dimension_indicator: 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)
      index_embeddings = self.embed(target_dimension_indicator)
      # assert_shape(index_embeddings, (-1, self.target_dim, self.embed_dim))
  
      # (batch_size, seq_len, target_dim * embed_dim)
      repeated_index_embeddings = (
          index_embeddings.unsqueeze(1)
          .expand(-1, unroll_length, -1, -1)
          .reshape(
              (-1, unroll_length, self.target_dim * self.embed_dim)
          )
      )
  
      # (batch_size, sub_seq_len, input_dim)
      inputs = torch.cat(
          (input_lags, repeated_index_embeddings, 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 torch.nn.modules import loss
  from typing import List, Optional, Tuple, Union
  import torch
  import torch.nn
  from gluonts.core.component import validated
  from pts.model import weighted_average
  from pts.modules import GaussianDiffusion, DiffusionOutput, MeanScaler, NOPScaler
  from .epsilon_theta import EpsilonTheta
label_desc: Use image node_id 3 for calling the TimeGradTrainingNetwork obj's underlying member method code with example usage: obj.unroll(lags, scale, time_feat, target_dimension_indicator, unroll_length, begin_state) and returns: outputs,  state,  lags_scaled,  inputs