class_docstr: We need the stack function `batchify_with_dict` in order to pass the features at the aggregated level properly
  during prediction. Gluonts does not allow this without changing the line corresponding to the
  `InferenceDataLoader` in the `predict` function.
code: def __init__(
      self,
      prediction_net: mx.gluon.HybridBlock,
      batch_size: int,
      prediction_length: int,
      ctx: mx.Context,
      input_transform: Transformation,
      lead_time: int = 0,
      forecast_generator: ForecastGenerator = SampleForecastGenerator(),
      output_transform: Optional[
          Callable[[DataEntry, np.ndarray], np.ndarray]
      ] = None,
      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,
      )
  
imports: from functools import partial
  from typing import Callable, Iterator, List, Optional
  from pathlib import Path
  import mxnet
  import numpy
  from gluonts.core.component import Type, validated
  from gluonts.core.serde import load_json
  from gluonts.dataset.common import DataEntry, Dataset
  from gluonts.dataset.loader import DataBatch, InferenceDataLoader
  from gluonts.model.forecast import Forecast
  from gluonts.model.forecast_generator import ForecastGenerator, SampleForecastGenerator
  from gluonts.mx.batchify import stack
  from gluonts.mx.context import get_mxnet_context
  from gluonts.mx.distribution import DistributionOutput, StudentTOutput
  from gluonts.mx.model.deepar import DeepAREstimator
  from gluonts.mx.model.deepar._network import DeepARPredictionNetwork
  from gluonts.mx.model.predictor import RepresentableBlockPredictor
  from gluonts.mx.trainer import Trainer
  from gluonts.mx.util import import_repr_block
  from gluonts.time_feature import TimeFeature
  from gluonts.transform import InstanceSampler, Transformation
  from gluonts.transform.feature import MissingValueImputation
label_desc: Use image node_id 1 to create a new RepresentableBlockPredictorBatchifyWithDict object from inherited base classes: RepresentableBlockPredictor with example: obj = RepresentableBlockPredictorBatchifyWithDict(prediction_net, batch_size, prediction_length, ctx, input_transform, lead_time, forecast_generator, output_transform, dtype)