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 predict(
      self,
      dataset: Dataset,
      num_samples: Optional[int] = None,
      num_workers: Optional[int] = None,
      num_prefetch: Optional[int] = None,
      **kwargs,
  ) -> Iterator[Forecast]:
      inference_data_loader = InferenceDataLoader(
          dataset,
          transform=self.input_transform,
          batch_size=self.batch_size,
          stack_fn=partial(
              batchify_with_dict, ctx=self.ctx, dtype=self.dtype
          ),
      )
      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: 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 2 for calling the RepresentableBlockPredictorBatchifyWithDict obj's underlying member method code with example usage: obj.predict(dataset, num_samples, num_workers, num_prefetch) without return types