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 deserialize(
      cls, path: Path, ctx: Optional[mx.Context] = None
  ) -> "RepresentableBlockPredictorBatchifyWithDict":
      ctx = ctx if ctx is not None else get_mxnet_context()
  
      with mx.Context(ctx):
          # deserialize constructor parameters
          with (path / "parameters.json").open("r") as fp:
              parameters = load_json(fp.read())
  
          # deserialize transformation chain
          with (path / "input_transform.json").open("r") as fp:
              transform = load_json(fp.read())
  
          # deserialize prediction network
          prediction_net = import_repr_block(path, "prediction_net")
  
          # input_names is derived from the prediction_net
          if "input_names" in parameters:
              del parameters["input_names"]
  
          parameters["ctx"] = ctx
          return RepresentableBlockPredictorBatchifyWithDict(
              input_transform=transform,
              prediction_net=prediction_net,
              batch_size=parameters["batch_size"],
              prediction_length=parameters["prediction_length"],
              ctx=parameters["ctx"],
              dtype=parameters["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 3 for calling the RepresentableBlockPredictorBatchifyWithDict obj's underlying member method code with example usage: obj.deserialize(cls, path, ctx) and returns: RepresentableBlockPredictorBatchifyWithDict