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 __eq__(self, that):
      if type(self) != type(that):
          return False
  
      if not equals(self.prediction_length, that.prediction_length):
          return False
      if not equals(self.lead_time, that.lead_time):
          return False
      if not equals(self.input_transform, that.input_transform):
          return False
  
      return equals(
          self.prediction_net.collect_params(),
          that.prediction_net.collect_params(),
      )
  
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 6 for calling the GluonPredictor obj's underlying member method code with example usage: obj.__eq__(that) and returns: equals, False, False, False, False