code: def predict(  # type: ignore
      self,
      dataset: Dataset,
      num_samples: Optional[int] = None,
      **kwargs,
  ) -> Iterator[Forecast]:
      stack_fn = partial(
          batchify,
          ctx=self.ctx,
          dtype=self.dtype,
          variable_length=True,
          is_right_pad=False,
      )
      inference_data_loader = InferenceDataLoader(
          dataset,
          transform=self.input_transform,
          batch_size=self.batch_size,
          stack_fn=stack_fn,
      )
      with mx.Context(self.ctx):
          yield from self.forecast_generator(
              inference_data_loader=inference_data_loader,
              prediction_net=self.prediction_net,
              input_names=self.input_names,
              output_transform=self.output_transform,
              num_samples=num_samples,
          )
  
imports: import itertools
  from functools import partial
  from pathlib import Path
  from typing import List, Optional, Callable, Iterator, Type
  import mxnet
  import numpy
  from gluonts.core.serde import load_json
  from gluonts.dataset.common import DataEntry, Dataset
  from gluonts.dataset.loader import InferenceDataLoader
  from gluonts.model.forecast import Forecast
  from gluonts.model.forecast_generator import ForecastGenerator, SampleForecastGenerator
  from gluonts.mx.batchify import batchify
  from gluonts.mx.model.predictor import RepresentableBlockPredictor
  from gluonts.transform import Transformation
label_desc: Use image node_id 2 for calling the DeepRenewalProcessPredictor obj's underlying member method code with example usage: obj.predict(dataset, num_samples) without return types