code: def create_predictor(
      self,
      transformation: Transformation,
      trained_network: TimeGradTrainingNetwork,
      device: torch.device,
  ) -> Predictor:
      prediction_network = TimeGradPredictionNetwork(
          input_size=self.input_size,
          target_dim=self.target_dim,
          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,
          dropout_rate=self.dropout_rate,
          cardinality=self.cardinality,
          embedding_dimension=self.embedding_dimension,
          diff_steps=self.diff_steps,
          loss_type=self.loss_type,
          beta_end=self.beta_end,
          beta_schedule=self.beta_schedule,
          residual_layers=self.residual_layers,
          residual_channels=self.residual_channels,
          dilation_cycle_length=self.dilation_cycle_length,
          lags_seq=self.lags_seq,
          scaling=self.scaling,
          conditioning_length=self.conditioning_length,
          num_parallel_samples=self.num_parallel_samples,
      ).to(device)
  
      copy_parameters(trained_network, prediction_network)
      input_names = get_module_forward_input_names(prediction_network)
      prediction_splitter = self.create_instance_splitter("test")
  
      return PyTorchPredictor(
          input_transform=transformation + prediction_splitter,
          input_names=input_names,
          prediction_net=prediction_network,
          batch_size=self.trainer.batch_size,
          freq=self.freq,
          prediction_length=self.prediction_length,
          device=device,
      )
  
file_path: gluonts/src/gluonts/nursery/robust-mts-attack/pts/model/time_grad/time_grad_estimator.py
imports: from typing import List, Optional
  import torch
  from gluonts.dataset.field_names import FieldName
  from gluonts.time_feature import TimeFeature
  from gluonts.torch.model.predictor import PyTorchPredictor
  from gluonts.torch.util import copy_parameters
  from gluonts.model.predictor import Predictor
  from gluonts.transform import Transformation, Chain, InstanceSplitter, ExpectedNumInstanceSampler, ValidationSplitSampler, TestSplitSampler, RenameFields, AsNumpyArray, ExpandDimArray, AddObservedValuesIndicator, AddTimeFeatures, VstackFeatures, SetFieldIfNotPresent, TargetDimIndicator
  from pts import Trainer
  from pts.feature import fourier_time_features_from_frequency, lags_for_fourier_time_features_from_frequency
  from pts.model import PyTorchEstimator
  from pts.model.utils import get_module_forward_input_names
  from .time_grad_network import TimeGradTrainingNetwork, TimeGradPredictionNetwork
label_desc: Use image node_id 5 for calling the TimeGradEstimator obj's underlying member method code with example usage: obj.create_predictor(transformation, trained_network, device) and returns: PyTorchPredictor