code: def create_transformation(self) -> Transformation:
      remove_field_names = []
      if not self.use_feat_static_real:
          remove_field_names.append(FieldName.FEAT_STATIC_REAL)
      if not self.use_feat_dynamic_real:
          remove_field_names.append(FieldName.FEAT_DYNAMIC_REAL)
      if not self.use_feat_dynamic_cat:
          remove_field_names.append(FieldName.FEAT_DYNAMIC_CAT)
  
      return Chain(
          [RemoveFields(field_names=remove_field_names)]
          + (
              [
                  SetField(
                      output_field=FieldName.FEAT_STATIC_CAT, value=[0]
                  )
              ]
              if not self.use_feat_static_cat
              else []
          )
          + (
              [
                  SetField(
                      output_field=FieldName.FEAT_STATIC_REAL,
                      value=[0.0],
                  )
              ]
              if not self.use_feat_static_real
              else []
          )
          + [
              AsNumpyArray(
                  field=FieldName.FEAT_STATIC_CAT,
                  expected_ndim=1,
                  dtype=np.long,
              ),
              AsNumpyArray(
                  field=FieldName.FEAT_STATIC_REAL,
                  expected_ndim=1,
                  dtype=self.dtype,
              ),
              AsNumpyArray(
                  field=FieldName.TARGET,
                  # in the following line, we add 1 for the time dimension
                  expected_ndim=1 + len(self.distr_output.event_shape),
                  dtype=self.dtype,
              ),
              AsNumpyArray(
                  field="control",
                  # in the following line, we add 1 for the time dimension
                  expected_ndim=1
                  + len(self.control_output.event_shape),
                  dtype=self.dtype,
              ),
              AddObservedValuesIndicator(
                  target_field=FieldName.TARGET,
                  output_field=FieldName.OBSERVED_VALUES,
                  dtype=self.dtype,
              ),
              AddTimeFeatures(
                  start_field=FieldName.START,
                  target_field=FieldName.TARGET,
                  output_field=FieldName.FEAT_TIME,
                  time_features=self.time_features,
                  pred_length=self.prediction_length,
              ),
              AddAgeFeature(
                  target_field=FieldName.TARGET,
                  output_field=FieldName.FEAT_AGE,
                  pred_length=self.prediction_length,
                  log_scale=True,
                  dtype=self.dtype,
              ),
              VstackFeatures(
                  output_field=FieldName.FEAT_TIME,
                  input_fields=[FieldName.FEAT_TIME, FieldName.FEAT_AGE]
                  + (
                      [FieldName.FEAT_DYNAMIC_REAL]
                      if self.use_feat_dynamic_real
                      else []
                  )
                  + (
                      [FieldName.FEAT_DYNAMIC_CAT]
                      if self.use_feat_dynamic_cat
                      else []
                  ),
              ),
          ]
      )
  
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 2 for calling the CausalDeepAREstimator obj's underlying member method code with example usage: obj.create_transformation() and returns: Chain