class_docstr: Interface for training callback.
  
  .. versionadded:: 1.3.0
class_docstr_tok: ["Interface","for","training","callback",".","..","versionadded",":",":","1.3.0"]
docstr: Run before each iteration.  Returns True when training should stop. See
  :py:meth:`after_iteration` for details.
docstr_tok: ["Run","before","each","iteration",".","Returns","True","when","training","should","stop",".","See",":","py",":","meth",":","`","after_iteration","`","for","details","."]
code: def before_iteration(
      self, model: _Model, epoch: int, evals_log: EvalsLog
  ) -> bool:
      """Run before each iteration.  Returns True when training should stop. See
      :py:meth:`after_iteration` for details.
  
      """
      return False
  
imports: import collections
  import os
  import pickle
  from abc import ABC
  from typing import Any, Callable, Dict, List, Optional, Sequence, Tuple, TypeVar, Union, cast
  import numpy
  from .None import collective
  from .core import Booster, DMatrix, XGBoostError, _parse_eval_str
label_desc: Use image node_id 4 for calling the TrainingCallback obj's underlying member method code with example usage: obj.before_iteration(model, epoch, evals_log) and returns: False