code: def create_training_network(
      self, device: torch.device
  ) -> CausalDeepARTrainingNetwork:
      return CausalDeepARTrainingNetwork(
          input_size=self.input_size,
          num_layers=self.num_layers,
          num_cells=self.num_cells,
          cell_type=self.cell_type,
          history_length=self.history_length,
          context_length=self.context_length,
          prediction_length=self.prediction_length,
          distr_output=self.distr_output,
          control_output=self.control_output,
          dropout_rate=self.dropout_rate,
          cardinality=self.cardinality,
          embedding_dimension=self.embedding_dimension,
          lags_seq=self.lags_seq,
          scaling=self.scaling,
          dtype=self.dtype,
      ).to(device)
  
imports: from typing import List, Optional
  import numpy
  import torch
  import torch.nn
  from gluonts.core.component import validated
  from gluonts.dataset.field_names import FieldName
  from gluonts.time_feature import TimeFeature, get_lags_for_frequency, time_features_from_frequency_str
  from gluonts.transform import Transformation, Chain, RemoveFields, SetField, AsNumpyArray, AddObservedValuesIndicator, AddTimeFeatures, AddAgeFeature, VstackFeatures, InstanceSplitter, ValidationSplitSampler, TestSplitSampler, ExpectedNumInstanceSampler
  from gluonts.torch.util import copy_parameters
  from gluonts.torch.model.predictor import PyTorchPredictor
  from gluonts.torch.modules.distribution_output import DistributionOutput
  from gluonts.model.predictor import Predictor
  from pts.model.utils import get_module_forward_input_names
  from pts import Trainer
  from pts.model import PyTorchEstimator
  from pts.modules import StudentTOutput
  from .causal_deepar_network import CausalDeepARTrainingNetwork, CausalDeepARPredictionNetwork
label_desc: Use image node_id 4 for calling the CausalDeepAREstimator obj's underlying member method code with example usage: obj.create_training_network(device) and returns: CausalDeepARTrainingNetwork