class_docstr: Handle end-to-end training and deployment of user-provided TensorFlow code.
class_docstr_tok: ["Handle","end-to-end","training","and","deployment","of","user-provided","TensorFlow","code","."]
docstr: Disable Debugger Hook Config for ParameterServer (PS) as it is not supported in smdebug.
  
  Else, set default HookConfig
docstr_tok: ["Disable","Debugger","Hook","Config","for","ParameterServer","(","PS",")","as","it","is","not","supported","in","smdebug",".","Else",",","set","default","HookConfig"]
code: def _validate_and_set_debugger_configs(self):
      """Disable Debugger Hook Config for ParameterServer (PS) as it is not supported in smdebug.
  
      Else, set default HookConfig
      """
      super(TensorFlow, self)._validate_and_set_debugger_configs()
      ps_enabled = (
          "parameter_server" in self.distribution
          and self.distribution["parameter_server"].get(
              "enabled", False
          )
      )
      if ps_enabled:
          if (
              self.debugger_hook_config is not None
              or self.debugger_rule_configs is not None
          ):
              logger.info(
                  "Amazon SageMaker Debugger does not currently support "
                  "Parameter Server distribution"
              )
          self.debugger_hook_config = None
          self.debugger_rule_configs = None
  
imports: from __future__ import absolute_import
  import logging
  from typing import Optional, Union, Dict
  from packaging import version
  from sagemaker import image_uris, s3, utils
  from sagemaker.deprecations import renamed_kwargs
  from sagemaker.estimator import Framework, EstimatorBase
  import sagemaker.fw_utils
  from sagemaker.tensorflow import defaults
  from sagemaker.tensorflow.model import TensorFlowModel
  from sagemaker.transformer import Transformer
  from sagemaker.vpc_utils import VPC_CONFIG_DEFAULT
  from sagemaker.workflow import is_pipeline_variable
  from sagemaker.tensorflow.training_compiler.config import TrainingCompilerConfig
  from sagemaker.workflow.entities import PipelineVariable
  from sagemaker.utils import format_tags
label_desc: Use image node_id 9 for calling the TensorFlow obj's underlying member method code with example usage: obj._validate_and_set_debugger_configs() without return types