code: def __init__(
      self,
      prediction_net,
      batch_size: int,
      prediction_length: int,
      ctx: mx.Context,
      input_transform: Transformation,
      input_names: Optional[List[str]] = None,
      lead_time: int = 0,
      forecast_generator: ForecastGenerator = SampleForecastGenerator(),
      output_transform: Optional[
          Callable[[DataEntry, np.ndarray], np.ndarray]
      ] = DeepRenewalProcessSampleOutputTransform(),
      dtype: Type = np.float32,
  ) -> None:
      super().__init__(
          prediction_net=prediction_net,
          batch_size=batch_size,
          prediction_length=prediction_length,
          ctx=ctx,
          input_transform=input_transform,
          lead_time=lead_time,
          forecast_generator=forecast_generator,
          output_transform=output_transform,
          dtype=dtype,
      )
      if input_names is not None:
          self.input_names = input_names
  
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 1 to create a new DeepRenewalProcessPredictor object from inherited base classes: RepresentableBlockPredictor with example: obj = DeepRenewalProcessPredictor(prediction_net, batch_size, prediction_length, ctx, input_transform, input_names, lead_time, forecast_generator, output_transform, dtype)