class_docstr: Base Python class for gRPC debug server.
class_docstr_tok: ["Base","Python","class","for","gRPC","debug","server","."]
docstr: Request enabling a debug tensor watchpoint or breakpoint.
  
  This will let the server send a EventReply to the client side
  (i.e., the debugged TensorFlow runtime process) to request adding a watch
  key (i.e., <node_name>:<output_slot>:<debug_op>) to the list of enabled
  watch keys. The list applies only to debug ops with the attribute
  gated_grpc=True.
  
  To disable the watch, use `request_unwatch()`.
  
  Args:
    node_name: (`str`) name of the node that the to-be-watched tensor belongs
      to, e.g., "hidden/Weights".
    output_slot: (`int`) output slot index of the tensor to watch.
    debug_op: (`str`) name of the debug op to enable. This should not include
      any attribute substrings.
    breakpoint: (`bool`) Iff `True`, the debug op will block and wait until it
      receives an `EventReply` response from the server. The `EventReply`
      proto may carry a TensorProto that modifies the value of the debug op's
      output tensor.
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code: def request_watch(
      self, node_name, output_slot, debug_op, breakpoint=False
  ):  # pylint: disable=redefined-builtin
      """Request enabling a debug tensor watchpoint or breakpoint.
  
      This will let the server send a EventReply to the client side
      (i.e., the debugged TensorFlow runtime process) to request adding a watch
      key (i.e., <node_name>:<output_slot>:<debug_op>) to the list of enabled
      watch keys. The list applies only to debug ops with the attribute
      gated_grpc=True.
  
      To disable the watch, use `request_unwatch()`.
  
      Args:
        node_name: (`str`) name of the node that the to-be-watched tensor belongs
          to, e.g., "hidden/Weights".
        output_slot: (`int`) output slot index of the tensor to watch.
        debug_op: (`str`) name of the debug op to enable. This should not include
          any attribute substrings.
        breakpoint: (`bool`) Iff `True`, the debug op will block and wait until it
          receives an `EventReply` response from the server. The `EventReply`
          proto may carry a TensorProto that modifies the value of the debug op's
          output tensor.
      """
      self._debug_ops_state_change_queue.put(
          _state_change(
              debug_service_pb2.EventReply.DebugOpStateChange.READ_WRITE
              if breakpoint
              else debug_service_pb2.EventReply.DebugOpStateChange.READ_ONLY,
              node_name,
              output_slot,
              debug_op,
          )
      )
  
imports: import collections
  import json
  import queue
  import threading
  import time
  from concurrent import futures
  import grpc
  from tensorflow.core.debug import debug_service_pb2
  from tensorflow.core.framework import graph_pb2
  from tensorflow.python.debug.lib import debug_graphs
  from tensorflow.python.debug.lib import debug_service_pb2_grpc
  from tensorflow.python.platform import tf_logging
  from tensorflow.python.util import compat
label_desc: Use image node_id 9 for calling the EventListenerBaseServicer obj's underlying member method code with example usage: obj.request_watch(node_name, output_slot, debug_op, breakpoint) without return types