class_docstr: A plugin to visualize Fairness Indicators.
code: def _get_evaluation_result_from_remote_path(self, request):
      evaluation_output_path = request.args.get(
          "evaluation_output_path"
      )
      try:
          evaluation_output_path = six.ensure_text(
              evaluation_output_path
          )
      except (UnicodeDecodeError, AttributeError):
          pass
      try:
          eval_result = tfma.load_eval_result(
              os.path.dirname(evaluation_output_path),
              output_file_format=self._get_output_file_format(
                  evaluation_output_path
              ),
          )
          data = widget_view.convert_slicing_metrics_to_ui_input(
              eval_result.slicing_metrics
          )
      except (KeyError, json_format.ParseError) as error:
          logging.info(
              "Error while fetching evaluation data, %s", error
          )
          data = []
      return http_util.Respond(
          request, data, content_type="application/json"
      )
  
imports: from __future__ import absolute_import
  from __future__ import division
  from __future__ import print_function
  import os
  from absl import logging
  from tensorboard_plugin_fairness_indicators import metadata
  import six
  import tensorflow_model_analysis
  from tensorflow_model_analysis.addons.fairness.view import widget_view
  from werkzeug import wrappers
  from google.protobuf import json_format
  from tensorboard.backend import http_util
  from tensorboard.plugins import base_plugin
label_desc: Use image node_id 9 for calling the FairnessIndicatorsPlugin obj's underlying member method code with example usage: obj._get_evaluation_result_from_remote_path(request) and returns: http_util