class_docstr: This is the base estimator for gluoncv.auto.Estimators.
  
  Parameters
  ----------
  config : dict
      Config in nested dict.
  logger : logging.Logger
      Optional logger for this estimator, can be `None` when default setting is used.
  reporter : callable
      The reporter for metric checkpointing.
  name : str
      Optional name for the estimator.
  
  Attributes
  ----------
  _logger : logging.Logger
      The customized/default logger for this estimator.
  _logdir : str
      The temporary dir for logs.
  _cfg : autocfg.dataclass
      The configurations.
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docstr: Load the state from disk copy.
  
  Parameters
  ----------
  filename : str
      The file name to load from.
  ctx: str, default is 'auto'
      The context for reloaded model.
      'auto': use previously saved context type if still available, fallback
      to cpu if no gpu detected.
      Use `cpu` if no GPU available.
      'cpu': use cpu for inference regardless.
      'gpu': use as many gpus available as possible.
      [0, 2, 4, ...]: if a list or tuple of integers are provided, the context
      will be [gpu(0), gpu(2), gpu(4)...]
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code: def load(cls, filename, ctx="auto"):
      """Load the state from disk copy.
  
      Parameters
      ----------
      filename : str
          The file name to load from.
      ctx: str, default is 'auto'
          The context for reloaded model.
          'auto': use previously saved context type if still available, fallback
          to cpu if no gpu detected.
          Use `cpu` if no GPU available.
          'cpu': use cpu for inference regardless.
          'gpu': use as many gpus available as possible.
          [0, 2, 4, ...]: if a list or tuple of integers are provided, the context
          will be [gpu(0), gpu(2), gpu(4)...]
      """
      with open(filename, "rb") as fid:
          obj = pickle.load(fid)
          obj._logger.debug("Unpickled from %s", filename)
          new_ctx = _suggest_load_context(obj.net, ctx, obj.ctx)
          obj.reset_ctx(new_ctx)
          return obj
  
imports: import os
  import math
  import pickle
  import io
  import logging
  from datetime import datetime
  import numpy
  import pandas
  from ...utils import random
  from ...utils.filesystem import temporary_filename
  from .utils import _suggest_load_context
label_desc: Use image node_id 18 for calling the BaseEstimator obj's underlying member method code with example usage: obj.load(cls, filename, ctx) and returns: obj