code: def create_predictor(
      self,
      transformation: Transformation,
      trained_network: CanonicalTrainingNetwork,
  ) -> Predictor:
      prediction_splitter = self._create_instance_splitter("test")
  
      prediction_net = CanonicalPredictionNetwork(
          embedder=trained_network.embedder,
          model=trained_network.model,
          distr_output=trained_network.distr_output,
          is_sequential=trained_network.is_sequential,
          prediction_len=self.prediction_length,
          num_parallel_samples=self.num_parallel_samples,
          params=trained_network.collect_params(),
      )
  
      return RepresentableBlockPredictor(
          input_transform=transformation + prediction_splitter,
          prediction_net=prediction_net,
          batch_size=self.batch_size,
          prediction_length=self.prediction_length,
          ctx=self.trainer.ctx,
      )
  
imports: from functools import partial
  from typing import List
  from mxnet.gluon import HybridBlock, nn
  from gluonts.core.component import validated
  from gluonts.dataset.common import Dataset
  from gluonts.dataset.field_names import FieldName
  from gluonts.dataset.loader import DataLoader, TrainDataLoader, ValidationDataLoader
  from gluonts.model.predictor import Predictor
  from gluonts.mx.batchify import batchify
  from gluonts.mx.block.feature import FeatureEmbedder
  from gluonts.mx.block.rnn import RNN
  from gluonts.mx.distribution import DistributionOutput, StudentTOutput
  from gluonts.mx.model.estimator import GluonEstimator
  from gluonts.mx.model.predictor import RepresentableBlockPredictor
  from gluonts.mx.trainer import Trainer
  from gluonts.mx.util import get_hybrid_forward_input_names
  from gluonts.time_feature import time_features_from_frequency_str
  from gluonts.transform import AddTimeFeatures, AsNumpyArray, InstanceSplitter, SelectFields, SetFieldIfNotPresent, TestSplitSampler, Transformation, ValidationSplitSampler
  from ._network import CanonicalPredictionNetwork, CanonicalTrainingNetwork
label_desc: Use image node_id 7 for calling the CanonicalEstimator obj's underlying member method code with example usage: obj.create_predictor(transformation, trained_network) and returns: RepresentableBlockPredictor