code: def accuracy_wilson(y_true, y_pred):
      """Return Wilson Interval bounds for accuracy metric.
  
      Parameters
      ----------
      y_true : array-like of shape (n_samples,)
          Ground truth labels
      y_pred : array-like of shape (n_samples,)
          Predicted labels
  
      Returns
      -------
          np.ndarray
          Array of length 2 of form: [lower_bound, upper_bound]
      """
      check_consistent_length(y_true, y_pred)
      score = accuracy_score(y_true, y_pred)
      bounds = general_wilson(
          score, len(y_true), digits_of_precision, z_score
      )
      return bounds
  
imports: import matplotlib
  import matplotlib.pyplot
  import numpy
  import pytest
  from sklearn.metrics import accuracy_score, confusion_matrix, recall_score
  from sklearn.utils import check_consistent_length
  from fairlearn.experimental.enable_metric_frame_plotting import plot_metric_frame
  from fairlearn.metrics import MetricFrame
  from fairlearn.metrics._plotter import _CONF_INTERVALS_FLIPPED_BOUNDS_ERROR, _CONF_INTERVALS_MUST_BE_ARRAY, _METRIC_FRAME_INVALID_ERROR
  from .data_for_test import g_1, y_p, y_t
label_desc: Use image node_id 3 for calling a global function with example usage: accuracy_wilson(y_true, y_pred) and returns: bounds