class_docstr: A predictor which serializes the network structure as an MXNet symbolic
  graph. Should be used for models deployed in production in order to ensure
  forward-compatibility as GluonTS models evolve.
  
  Used by the training shell if training is invoked with a hyperparameter
  `use_symbol_block_predictor = True`.
code: def as_symbol_block_predictor(
      self,
      batch: Optional[DataBatch] = None,
      dataset: Optional[Dataset] = None,
  ) -> "SymbolBlockPredictor":
      return self
  
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 1 for calling the SymbolBlockPredictor obj's underlying member method code with example usage: obj.as_symbol_block_predictor(batch, dataset) and returns: self