docstr: Args:
      target, config: same as :meth:`Session.__init__()`.
      config: a :class:`tf.ConfigProto` instance, defaults to :func:`tfutils.get_default_sess_config()`
docstr_tok: ["Args",":","target",",","config",":","same","as",":","meth",":","`","Session.__init__","(",")","`",".","config",":","a",":","class",":","`","tf.ConfigProto","`","instance",",","defaults","to",":","func",":","`","tfutils.get_default_sess_config","(",")","`"]
code: def __init__(self, target="", config=None):
      """
      Args:
          target, config: same as :meth:`Session.__init__()`.
          config: a :class:`tf.ConfigProto` instance, defaults to :func:`tfutils.get_default_sess_config()`
      """
      self.target = target
  
      if config is None:
          # distributed trainer doesn't support user-provided config
          # we set this attribute so that they can check
          self.user_provided_config = False
          config = get_default_sess_config()
      else:
          self.user_provided_config = True
          logger.warn(_WRN1)
          logger.warn(_WRN2)
      self.config = config
  
file_path: tensorpack/tensorpack/tfutils/sesscreate.py
imports: from ..compat import tfv1
  from ..utils import logger
  from .common import get_default_sess_config
label_desc: Use image node_id 1 to create a new NewSessionCreator object from inherited base classes: tf.train with example: obj = NewSessionCreator(target, config)