class_docstr: A predictor which serializes the network structure using the JSON-
  serialization methods located in `gluonts.core.serde`. Use the following
  logic to create a `RepresentableBlockPredictor` from a trained prediction
  network.
  
  >>> def create_representable_block_predictor(
  ...        prediction_network: mx.gluon.HybridBlock,
  ...        **kwargs
  ... ) -> RepresentableBlockPredictor:
  ...    return RepresentableBlockPredictor(
  ...        prediction_net=prediction_network,
  ...        **kwargs
  ...    )
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code: def deserialize(  # type: ignore
      cls, path: Path, ctx: Optional[mx.Context] = None
  ) -> "RepresentableBlockPredictor":
      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 RepresentableBlockPredictor(
              input_transform=transform,
              prediction_net=prediction_net,
              **parameters,
          )
  
imports: import logging
  from functools import partial
  from pathlib import Path
  from typing import Callable, Iterator, List, Optional, Type
  import mxnet
  import numpy
  from gluonts.core.serde import dump_json, 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, to_numpy
  from gluonts.model.predictor import OutputTransform, Predictor
  from gluonts.mx.batchify import batchify
  from gluonts.mx.component import equals
  from gluonts.mx.context import get_mxnet_context
  from gluonts.mx.util import export_repr_block, export_symb_block, get_hybrid_forward_input_names, hybrid_block_to_symbol_block, import_repr_block, import_symb_block
  from gluonts.transform import Transformation
label_desc: Use image node_id 5 for calling the RepresentableBlockPredictor obj's underlying member method code with example usage: obj.deserialize(cls, path, ctx) and returns: RepresentableBlockPredictor