class_docstr: Module implementing the LagTST model for forecasting.
  
  Parameters
  ----------
  prediction_length
      Number of time points to predict.
  context_length
      Number of time steps prior to prediction time that the model.
  distr_output
      Distribution to use to evaluate observations and sample predictions.
      Default: ``StudentTOutput()``.
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code: def describe_inputs(self, batch_size=1) -> InputSpec:
      return InputSpec(
          {
              "past_target": Input(
                  shape=(batch_size, self._past_length),
                  dtype=torch.float,
              ),
              "past_observed_values": Input(
                  shape=(batch_size, self._past_length),
                  dtype=torch.float,
              ),
          },
          torch.zeros,
      )
  
file_path: gluonts/src/gluonts/torch/model/lag_tst/module.py
imports: from typing import Tuple, Optional, List
  import torch
  from torch import nn
  from gluonts.core.component import validated
  from gluonts.model import Input, InputSpec
  from gluonts.torch.distributions import StudentTOutput
  from gluonts.torch.scaler import StdScaler, MeanScaler, NOPScaler
  from gluonts.torch.util import unsqueeze_expand, lagged_sequence_values
  from gluonts.time_feature import get_lags_for_frequency
  from gluonts.torch.model.simple_feedforward import make_linear_layer
  from gluonts.torch.model.patch_tst import SinusoidalPositionalEmbedding
  from gluonts.torch.util import weighted_average
label_desc: Use image node_id 3 for calling the LagTSTModel obj's underlying member method code with example usage: obj.describe_inputs(batch_size) and returns: InputSpec