class_docstr: Base predictor type for Gluon-based models.
  
  Parameters
  ----------
  input_names
      Input tensor names for the graph
  prediction_net
      Network that will be called for prediction
  batch_size
      Number of time series to predict in a single batch
  prediction_length
      Number of time steps to predict
  input_transform
      Input transformation pipeline
  output_transform
      Output transformation
  ctx
      MXNet context to use for computation
  forecast_generator
      Class to generate forecasts from network outputs
docstr: Returns a variant of the current :class:`GluonPredictor` backed
  by a Gluon `SymbolBlock`. If the current predictor is already a
  :class:`SymbolBlockPredictor`, it just returns itself.
  
  One of batch or datset must be set.
  
  Parameters
  ----------
  batch
      A batch of data to use for the required forward pass after the
      `hybridize()` call of the underlying network.
  dataset
      Dataset from which a batch is extracted if batch is not set.
  
  Returns
  -------
  SymbolBlockPredictor
      A predictor derived from the current one backed by a `SymbolBlock`.
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code: def as_symbol_block_predictor(
      self,
      batch: Optional[DataBatch] = None,
      dataset: Optional[Dataset] = None,
  ) -> "SymbolBlockPredictor":
      """
      Returns a variant of the current :class:`GluonPredictor` backed
      by a Gluon `SymbolBlock`. If the current predictor is already a
      :class:`SymbolBlockPredictor`, it just returns itself.
  
      One of batch or datset must be set.
  
      Parameters
      ----------
      batch
          A batch of data to use for the required forward pass after the
          `hybridize()` call of the underlying network.
      dataset
          Dataset from which a batch is extracted if batch is not set.
  
      Returns
      -------
      SymbolBlockPredictor
          A predictor derived from the current one backed by a `SymbolBlock`.
      """
      raise NotImplementedError
  
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 4 for calling the GluonPredictor obj's underlying member method code with example usage: obj.as_symbol_block_predictor(batch, dataset) without return types