class_docstr: It maintains the global step in the graph, making sure it's increased by one at every `hooked_sess.run`.
  This callback is used internally by the trainer, you don't need to worry about it.
class_docstr_tok: ["It","maintains","the","global","step","in","the","graph",",","making","sure","it","'s","increased","by","one","at","every","`","hooked_sess.run","`",".","This","callback","is","used","internally","by","the","trainer",",","you","do","n't","need","to","worry","about","it","."]
code: def _after_run(self, _, __):
      # Keep python-side global_step in agreement with TF-side
      self.trainer.loop._global_step += 1
  
imports: import tqdm
  from six.moves import zip
  from ..compat import tfv1
  from ..tfutils.common import get_global_step_var, get_op_tensor_name
  from ..utils import logger
  from ..utils.naming import GLOBAL_STEP_INCR_OP_NAME
  from ..utils.utils import get_tqdm_kwargs
  from .base import Callback
label_desc: Use image node_id 4 for calling the MaintainStepCounter obj's underlying member method code with example usage: obj._after_run(_, __) without return types