class_docstr: A plugin that serves 3D visualization of meshes.
class_docstr_tok: ["A","plugin","that","serves","3D","visualization","of","meshes","."]
docstr: A route (HTTP handler) that returns a response with tags.
  
  Args:
    request: The werkzeug.Request object.
  
  Returns:
    A response that contains a JSON object. The keys of the object
    are all the runs. Each run is mapped to a (potentially empty)
    list of all tags that are relevant to this plugin.
docstr_tok: ["A","route","(","HTTP","handler",")","that","returns","a","response","with","tags",".","Args",":","request",":","The","werkzeug.Request","object",".","Returns",":","A","response","that","contains","a","JSON","object",".","The","keys","of","the","object","are","all","the","runs",".","Each","run","is","mapped","to","a","(","potentially","empty",")","list","of","all","tags","that","are","relevant","to","this","plugin","."]
code: def _serve_tags(self, request):
      """A route (HTTP handler) that returns a response with tags.
  
      Args:
        request: The werkzeug.Request object.
  
      Returns:
        A response that contains a JSON object. The keys of the object
        are all the runs. Each run is mapped to a (potentially empty)
        list of all tags that are relevant to this plugin.
      """
      ctx = plugin_util.context(request.environ)
      experiment = plugin_util.experiment_id(request.environ)
      all_runs = self._data_provider.list_tensors(
          ctx,
          experiment_id=experiment,
          plugin_name=metadata.PLUGIN_NAME,
      )
  
      # tagToContent is itself a dictionary mapping tag name to string
      # SummaryMetadata.plugin_data.content. Retrieve the keys of that dictionary
      # to obtain a list of tags associated with each run. For each tag estimate
      # number of samples.
      response = dict()
      for run, tags in all_runs.items():
          response[run] = dict()
          for instance_tag, metadatum in tags.items():
              md = metadata.parse_plugin_metadata(
                  metadatum.plugin_content
              )
              if not self._version_checker.ok(
                  md.version, run, instance_tag
              ):
                  continue
              # Make sure we only operate on user-defined tags here.
              tag = self._tag(ctx, experiment, run, instance_tag)
              meta = self._instance_tag_metadata(
                  ctx, experiment, run, instance_tag
              )
              # Batch size must be defined, otherwise we don't know how many
              # samples were there.
              response[run][tag] = {"samples": meta.shape[0]}
      return http_util.Respond(request, response, "application/json")
  
imports: import numpy
  from werkzeug import wrappers
  from tensorboard.backend import http_util
  from tensorboard.data import provider
  from tensorboard.plugins import base_plugin
  from tensorboard.plugins.mesh import metadata
  from tensorboard.plugins.mesh import plugin_data_pb2
  from tensorboard import plugin_util
label_desc: Use image node_id 5 for calling the MeshPlugin obj's underlying member method code with example usage: obj._serve_tags(request) and returns: http_util