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
code: def __init__(
      self,
      input_names: List[str],
      prediction_net,
      batch_size: int,
      prediction_length: int,
      ctx: mx.Context,
      input_transform: Transformation,
      lead_time: int = 0,
      forecast_generator: ForecastGenerator = SampleForecastGenerator(),
      output_transform: Optional[OutputTransform] = None,
      dtype: Type = np.float32,
  ) -> None:
      super().__init__(
          lead_time=lead_time,
          prediction_length=prediction_length,
      )
  
      self.input_names = input_names
      self.prediction_net = prediction_net
      self.batch_size = batch_size
      self.input_transform = input_transform
      self.forecast_generator = forecast_generator
      self.output_transform = output_transform
      self.ctx = ctx
      self.dtype = dtype
  
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 to create a new GluonPredictor object from inherited base classes: Predictor with example: obj = GluonPredictor(input_names, prediction_net, batch_size, prediction_length, ctx, input_transform, lead_time, forecast_generator, output_transform, dtype)