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.
class_docstr_tok: ["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","."]
docstr: Reset model context.
  
  Parameters
  ----------
  ctx : list or ctx
      The desired new ctx list, the type must match the network.
      For example, if using mxnet, the ctx must be `mxnet.Context`.
docstr_tok: ["Reset","model","context",".","Parameters","--","--","--","--","--","ctx",":","list","or","ctx","The","desired","new","ctx","list",",","the","type","must","match","the","network",".","For","example",",","if","using","mxnet",",","the","ctx","must","be","`","mxnet.Context","`","."]
code: def reset_ctx(self, ctx=None):
      """Reset model context.
  
      Parameters
      ----------
      ctx : list or ctx
          The desired new ctx list, the type must match the network.
          For example, if using mxnet, the ctx must be `mxnet.Context`.
  
      """
      if not ctx:
          return
      if not isinstance(ctx, (tuple, list)):
          ctx_list = [ctx]
      else:
          ctx_list = ctx
      done = False
      try:
          import mxnet as mx
  
          if isinstance(self.net, mx.gluon.Block):
              for c in ctx_list:
                  assert isinstance(c, mx.Context)
              if hasattr(self.net, "reset_ctx"):
                  self.net.reset_ctx(ctx_list)
              else:
                  self.net.collect_params().reset_ctx(ctx_list)
              self.ctx = ctx_list
              done = True
      except ImportError:
          pass
      try:
          import torch
  
          if isinstance(
              self.net, (torch.nn.Module, torch.nn.DataParallel)
          ):
              for c in ctx_list:
                  assert isinstance(c, torch.device)
              if hasattr(self.net, "reset_ctx"):
                  self.net.reset_ctx(ctx_list)
              else:
                  if isinstance(self.net, torch.nn.DataParallel):
                      self.net = torch.nn.DataParallel(
                          self.net.module,
                          device_ids=[ctx.index for ctx in ctx_list],
                      )
                  self.net.to(self.ctx[0])
              self.ctx = ctx_list
              done = True
      except ImportError:
          pass
      if not done:
          raise RuntimeError(
              "Unable to reset_ctx, no `mxnet` and `pytorch`."
          )
  
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 16 for calling the BaseEstimator obj's underlying member method code with example usage: obj.reset_ctx(ctx) without return types