code: def unroll_encoder(
      self,
      feat_static_cat: torch.Tensor,  # (batch_size, num_features)
      feat_static_real: torch.Tensor,  # (batch_size, num_features)
      past_time_feat: torch.Tensor,  # (batch_size, history_length, num_features)
      past_target: torch.Tensor,  # (batch_size, history_length, *target_shape)
      past_observed_values: torch.Tensor,  # (batch_size, history_length, *target_shape)
      past_control: torch.Tensor,
      future_control: Optional[torch.Tensor] = None,
      future_time_feat: Optional[
          torch.Tensor
      ] = None,  # (batch_size, prediction_length, num_features)
      future_target: Optional[
          torch.Tensor
      ] = None,  # (batch_size, prediction_length, *target_shape)
  ) -> Tuple[
      torch.Tensor,
      Union[torch.Tensor, List],
      torch.Tensor,
      torch.Tensor,
  ]:
      if (
          future_time_feat is None
          or future_target is None
          or future_control is None
      ):
          time_feat = past_time_feat[
              :, self.history_length - self.context_length :, ...
          ]
          sequence = past_target
          control_sequence = past_control
          sequence_length = self.history_length
          subsequences_length = self.context_length
      else:
          time_feat = torch.cat(
              (
                  past_time_feat[
                      :,
                      self.history_length - self.context_length :,
                      ...,
                  ],
                  future_time_feat,
              ),
              dim=1,
          )
          sequence = torch.cat((past_target, future_target), dim=1)
          control_sequence = torch.cat(
              (past_control, future_control), dim=1
          )
          sequence_length = self.history_length + self.prediction_length
          subsequences_length = (
              self.context_length + self.prediction_length
          )
  
      lags = self.get_lagged_subsequences(
          sequence=sequence,
          sequence_length=sequence_length,
          indices=self.lags_seq,
          subsequences_length=subsequences_length,
      )
  
      control_lags = self.get_lagged_subsequences(
          sequence=control_sequence,
          sequence_length=sequence_length,
          indices=self.lags_seq,
          subsequences_length=subsequences_length,
      )
  
      # scale is computed on the context length last units of the past target
      # scale shape is (batch_size, 1, *target_shape)
      _, scale = self.scaler(
          past_target[:, -self.context_length :, ...],
          past_observed_values[:, -self.context_length :, ...],
      )
  
      _, control_scale = self.control_scaler(
          past_control[:, self.context_length :, ...],
          past_observed_values[:, self.context_length :, ...],
      )
  
      # (batch_size, num_features)
      embedded_cat = self.embedder(feat_static_cat)
  
      # in addition to embedding features, use the log scale as it can help
      # prediction too
      # (batch_size, num_features + prod(target_shape))
      static_feat = torch.cat(
          (
              embedded_cat,
              feat_static_real,
              scale.log()
              if len(self.target_shape) == 0
              else scale.squeeze(1).log(),
              control_scale.log()
              if len(self.target_shape) == 0
              else control_scale.squeeze(1).log(),
          ),
          dim=1,
      )
  
      # (batch_size, subsequences_length, num_features + 1)
      repeated_static_feat = static_feat.unsqueeze(1).expand(
          -1, subsequences_length, -1
      )
  
      # (batch_size, sub_seq_len, *target_shape, num_lags)
      lags_scaled = lags / scale.unsqueeze(-1)
      control_lags_scaled = control_lags / control_scale.unsqueeze(-1)
  
      # from (batch_size, sub_seq_len, *target_shape, num_lags)
      # to (batch_size, sub_seq_len, prod(target_shape) * num_lags)
      input_lags = lags_scaled.reshape(
          (
              -1,
              subsequences_length,
              len(self.lags_seq) * prod(self.target_shape),
          )
      )
  
      input_control_lags = control_lags_scaled.reshape(
          (
              -1,
              subsequences_length,
              len(self.lags_seq) * prod(self.target_shape),
          )
      )
  
      # (batch_size, sub_seq_len, input_dim)
      inputs = torch.cat(
          (
              input_lags,
              input_control_lags,
              time_feat,
              repeated_static_feat,
          ),
          dim=-1,
      )
  
      # unroll encoder
      outputs, state = self.rnn(inputs)
  
      # outputs: (batch_size, seq_len, num_cells)
      # state: list of (num_layers, batch_size, num_cells) tensors
      # scale: (batch_size, 1, *target_shape)
      # static_feat: (batch_size, num_features + prod(target_shape))
      return outputs, state, scale, control_scale, static_feat
  
imports: from typing import List, Optional, Tuple, Union
  import numpy
  import torch
  import torch.nn
  from torch.distributions import Distribution
  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
label_desc: Use image node_id 3 for calling the CausalDeepARNetwork obj's underlying member method code with example usage: obj.unroll_encoder(feat_static_cat, feat_static_real, past_time_feat, past_target, past_observed_values, past_control, future_control, future_time_feat, future_target) and returns: outputs,  state,  scale,  control_scale,  static_feat