class_docstr: Visualization for standard_artifacts.ModelEvaluation.
code: def display(self, artifact: types.Artifact):
      from IPython.core.display import (
          display,
      )  # pylint: disable=g-import-not-at-top
      from IPython.core.display import (
          HTML,
      )  # pylint: disable=g-import-not-at-top
  
      # Workaround needed to improve TFMA notebook visualization legibility in
      # dark mode.
      display(
          HTML(
              "<style>html[theme=dark] iframe {background: white;}</style>"
          )
      )
      tfma_result = tfma.load_eval_result(artifact.uri)
      # TODO(ccy): add comment instructing user to use the TFMA library directly
      # in order to render non-default slicing metric views.
      if (
          view := tfma.view.render_slicing_metrics(tfma_result)
      ) is not None:
          display(view)
  
imports: import os
  import tensorflow_data_validation
  import tensorflow_model_analysis
  from tfx import types
  from tfx.components.statistics_gen import stats_artifact_utils
  from tfx.orchestration.experimental.interactive import visualizations
  from tfx.types import artifact_utils
  from tfx.types import standard_artifacts
  from tfx.utils import io_utils
  from tensorflow_metadata.proto.v0 import anomalies_pb2
label_desc: Use image node_id 1 for calling the ModelEvaluationVisualization obj's underlying member method code with example usage: obj.display(artifact) without return types