code: def create_transformation(self) -> Transformation:
      remove_field_names = []
      if self.num_feat_static_real == 0:
          remove_field_names.append(FieldName.FEAT_STATIC_REAL)
      if self.num_feat_dynamic_real == 0:
          remove_field_names.append(FieldName.FEAT_DYNAMIC_REAL)
      return Chain(
          [
              RemoveFields(field_names=remove_field_names),
              SetField(
                  output_field=FieldName.FEAT_STATIC_CAT, value=[0]
              )
              if self.num_feat_static_cat == 0
              else Identity(),
              SetField(
                  output_field=FieldName.FEAT_STATIC_REAL, value=[0.0]
              )
              if self.num_feat_static_real == 0
              else Identity(),
              AsNumpyArray(
                  field=FieldName.FEAT_STATIC_CAT,
                  expected_ndim=1,
                  dtype=int,
              ),
              AsNumpyArray(
                  field=FieldName.FEAT_STATIC_REAL,
                  expected_ndim=1,
                  dtype=np.float32,
              ),
              AsNumpyArray(field=FieldName.TARGET, expected_ndim=1),
              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,
              ),
              VstackFeatures(
                  output_field=FieldName.FEAT_TIME,
                  input_fields=[FieldName.FEAT_TIME, FieldName.FEAT_AGE]
                  + (
                      [FieldName.FEAT_DYNAMIC_REAL]
                      if self.num_feat_dynamic_real > 0
                      else []
                  ),
              ),
              AsNumpyArray(
                  FieldName.FEAT_TIME, expected_ndim=2, dtype=np.float32
              ),
          ]
      )
  
imports: from typing import Any, Dict, List, Optional, Iterable
  import lightning.pytorch
  import torch
  import numpy
  from gluonts.core.component import validated
  from gluonts.dataset.common import Dataset
  from gluonts.dataset.field_names import FieldName
  from gluonts.dataset.loader import as_stacked_batches
  from gluonts.itertools import Cyclic
  from gluonts.torch.model.estimator import PyTorchLightningEstimator
  from gluonts.torch.model.predictor import PyTorchPredictor
  from gluonts.time_feature import get_seasonality, time_features_from_frequency_str, TimeFeature
  from gluonts.transform import Transformation, AddAgeFeature, AddObservedValuesIndicator, AddTimeFeatures, AsNumpyArray, Chain, ExpectedNumInstanceSampler, InstanceSampler, InstanceSplitter, SetField, RemoveFields, QuantizeMeanScaled, VstackFeatures, Identity, TestSplitSampler, ValidationSplitSampler
  from gluonts.maybe import unwrap_or
  from .module import WaveNet
  from .lightning_module import WaveNetLightningModule
label_desc: Use image node_id 2 for calling the WaveNetEstimator obj's underlying member method code with example usage: obj.create_transformation() and returns: Chain