code: def plot_performance_and_fairness(
      fobject,
      other_objects=None,
      fairness_metric="TPR",
      performance_metric="accuracy",
      title=None,
      verbose=True,
      **kwargs,
  ):
      data = utils._unwrap_parity_loss_data(
          fobject, other_objects, [fairness_metric], verbose
      )
      assert len(data.label.unique()) == len(data.label)
  
      performance_data = pd.DataFrame(
          columns=["label", "performance_score"]
      )
      performance_data.loc[0] = [
          fobject.label,
          utils._classification_performance(
              fobject, verbose, performance_metric
          ),
      ]
  
      if other_objects:
          for i, obj in enumerate(other_objects):
              performance_data.loc[i + 1] = [
                  obj.label,
                  utils._classification_performance(
                      obj, verbose, performance_metric
                  ),
              ]
  
      data = data.merge(performance_data, on="label")
  
      fig = px.scatter(
          data,
          x="performance_score",
          y="score",
          color="label",
          custom_data=[data.label],
          color_discrete_sequence=_theme.get_default_colors(
              len(data.label), "line"
          ),
      )
  
      fig.update_traces(
          mode="markers", marker=dict(size=[15 for _ in data.label])
      )
  
      if title is None:
          title = "Performance and Fairness"
      fig.update_layout(utils._fairness_theme(title))
  
      fig.update_yaxes(
          title="reversed " + fairness_metric + " parity loss",
          autorange="reversed",
      )
      fig.update_xaxes(title=performance_metric)
  
      fig.update_traces(
          hovertemplate="<br>".join(
              [
                  "<b>%{customdata[0]}</b><br>",
                  fairness_metric + " parity loss: %{y:.3f}",
                  performance_metric + " score: %{x:.3f}",
                  "<extra></extra>",
              ]
          )
      )
  
      return fig
  
imports: import numpy
  import pandas
  import plotly.express
  import plotly.graph_objects
  from plotly.subplots import make_subplots
  from .None import checks
  from .None import utils
  from .._basics import checks
  from .._basics.exceptions import ParameterCheckError
  from ...None import _theme
  from ..._explainer import helper
label_desc: Use image node_id 7 for calling a global function with example usage: plot_performance_and_fairness(fobject, other_objects, fairness_metric, performance_metric, title, verbose) and returns: fig