class_docstr: Option Builder for Profiling API.
  
  For tutorial on the options, see
  https://github.com/tensorflow/tensorflow/tree/master/tensorflow/core/profiler/g3doc/options.md
  
  ```python
  # Users can use pre-built options:
  opts = (
      tf.profiler.ProfileOptionBuilder.trainable_variables_parameter())
  
  # Or, build your own options:
  opts = (tf.compat.v1.profiler.ProfileOptionBuilder()
      .with_max_depth(10)
      .with_min_micros(1000)
      .select(['accelerator_micros'])
      .with_stdout_output()
      .build()
  
  # Or customize the pre-built options:
  opts = (tf.compat.v1.profiler.ProfileOptionBuilder(
      tf.profiler.ProfileOptionBuilder.time_and_memory())
      .with_displaying_options(show_name_regexes=['.*rnn.*'])
      .build())
  
  # Finally, profiling with the options:
  _ = tf.compat.v1.profiler.profile(tf.compat.v1.get_default_graph(),
                          run_meta=run_meta,
                          cmd='scope',
                          options=opts)
  ```
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docstr: Selectively counting statistics based on node types.
  
  Here, 'types' means the profiler nodes' properties. Profiler by default
  consider device name (e.g. /job:xx/.../device:GPU:0) and operation type
  (e.g. MatMul) as profiler nodes' properties. User can also associate
  customized 'types' to profiler nodes through OpLogProto proto.
  
  For example, user can select profiler nodes placed on gpu:0 with:
  `account_type_regexes=['.*gpu:0.*']`
  
  If none of a node's properties match the specified regexes, the node is
  not displayed nor accounted.
  
  Args:
    account_type_regexes: A list of regexes specifying the types.
  Returns:
    self.
docstr_tok: ["Selectively","counting","statistics","based","on","node","types",".","Here",",","'types","'","means","the","profiler","nodes","'","properties",".","Profiler","by","default","consider","device","name","(","e.g",".","\/job",":","xx\/","...","\/device",":","GPU:0",")","and","operation","type","(","e.g",".","MatMul",")","as","profiler","nodes","'","properties",".","User","can","also","associate","customized","'types","'","to","profiler","nodes","through","OpLogProto","proto",".","For","example",",","user","can","select","profiler","nodes","placed","on","gpu:0","with",":","`","account_type_regexes=","[","'.","*","gpu:0",".","*","'","]","`","If","none","of","a","node","'s","properties","match","the","specified","regexes",",","the","node","is","not","displayed","nor","accounted",".","Args",":","account_type_regexes",":","A","list","of","regexes","specifying","the","types",".","Returns",":","self","."]
code: def with_accounted_types(self, account_type_regexes):
      """Selectively counting statistics based on node types.
  
      Here, 'types' means the profiler nodes' properties. Profiler by default
      consider device name (e.g. /job:xx/.../device:GPU:0) and operation type
      (e.g. MatMul) as profiler nodes' properties. User can also associate
      customized 'types' to profiler nodes through OpLogProto proto.
  
      For example, user can select profiler nodes placed on gpu:0 with:
      `account_type_regexes=['.*gpu:0.*']`
  
      If none of a node's properties match the specified regexes, the node is
      not displayed nor accounted.
  
      Args:
        account_type_regexes: A list of regexes specifying the types.
      Returns:
        self.
      """
      self._options["account_type_regexes"] = copy.copy(
          account_type_regexes
      )
      return self
  
imports: import copy
  from tensorflow.python.profiler import tfprof_logger
  from tensorflow.python.util.tf_export import tf_export
label_desc: Use image node_id 12 for calling the ProfileOptionBuilder obj's underlying member method code with example usage: obj.with_accounted_types(account_type_regexes) and returns: self