class_docstr: Monitoring task that creates a sensible TensorBoard for a model.
  
  Monitors all the model's parameters for which their name matches with `keywords_to_monitor`.
  By default, "kernel" and "likelihood" are elements of `keywords_to_monitor`.
  
  Example::
  
      keyword = "kernel", parameter = "kernel.lengthscale" => match
      keyword = "variational", parameter = "kernel.lengthscale" => no match
class_docstr_tok: ["Monitoring","task","that","creates","a","sensible","TensorBoard","for","a","model",".","Monitors","all","the","model","'s","parameters","for","which","their","name","matches","with","`","keywords_to_monitor","`",".","By","default",",","``","kernel","''","and","``","likelihood","''","are","elements","of","`","keywords_to_monitor","`",".","Example",":",":","keyword","=","``","kernel","''",",","parameter","=","``","kernel.lengthscale","''","=",">","match","keyword","=","``","variational","''",",","parameter","=","``","kernel.lengthscale","''","=",">","no","match"]
code: def run(self, **unused_kwargs: Any) -> None:
      for name, parameter in parameter_dict(self.model).items():
          # check if the parameter name matches any of the specified keywords
          if self.summarize_all or any(
              keyword in name for keyword in self.keywords_to_monitor
          ):
              # keys are sometimes prepended with a character, which we strip
              name = name.lstrip(self.left_strip_character)
              self._summarize_parameter(name, parameter)
  
imports: from io import BytesIO
  from typing import TYPE_CHECKING, Any, Callable, Dict, List, Optional, Union
  import numpy
  import tensorflow
  from ..base import AnyNDArray, Parameter
  from ..models import BayesianModel
  from ..utilities import parameter_dict
  from .base import MonitorTask
label_desc: Use image node_id 2 for calling the ModelToTensorBoard obj's underlying member method code with example usage: obj.run() without return types