code: def create_predictor(
      self,
      transformation: Transformation,
      module: WaveNetLightningModule,
  ) -> PyTorchPredictor:
      prediction_splitter = self._create_instance_splitter("test")
  
      return PyTorchPredictor(
          input_transform=transformation + prediction_splitter,
          input_names=PREDICTION_INPUT_NAMES,
          prediction_net=module,
          batch_size=self.batch_size,
          prediction_length=self.prediction_length,
          device="auto",
      )
  
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 7 for calling the WaveNetEstimator obj's underlying member method code with example usage: obj.create_predictor(transformation, module) and returns: PyTorchPredictor