code: def create_transformation(self) -> Transformation:
      remove_field_names = [
          FieldName.FEAT_DYNAMIC_CAT,
      ]
      if not self.use_feat_dynamic_real:
          remove_field_names.append(FieldName.FEAT_DYNAMIC_REAL)
      if not self.use_feat_static_real:
          remove_field_names.append(FieldName.FEAT_STATIC_REAL)
      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),
              ),
              AddObservedValuesIndicator(
                  target_field=FieldName.TARGET,
                  output_field=FieldName.OBSERVED_VALUES,
              ),
              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,
              ),
              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 []
                  ),
              ),
          ]
      )
  
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
  from gluonts.torch.modules.distribution_output import DistributionOutput
  from gluonts.torch.util import copy_parameters
  from gluonts.torch.model.predictor import PyTorchPredictor
  from gluonts.model.predictor import Predictor
  from gluonts.transform import Transformation, Chain, InstanceSplitter, InstanceSampler, ValidationSplitSampler, TestSplitSampler, ExpectedNumInstanceSampler, RemoveFields, AddAgeFeature, AsNumpyArray, AddObservedValuesIndicator, AddTimeFeatures, VstackFeatures, SetField
  from pts import Trainer
  from pts.model.utils import get_module_forward_input_names
  from pts.feature import fourier_time_features_from_frequency, lags_for_fourier_time_features_from_frequency
  from pts.model import PyTorchEstimator
  from pts.modules import StudentTOutput
  from .transformer_network import TransformerTrainingNetwork, TransformerPredictionNetwork
label_desc: Use image node_id 2 for calling the TransformerEstimator obj's underlying member method code with example usage: obj.create_transformation() and returns: Chain