code: def decision_boundaries(
      decision_boundary_x,
      decision_boundary_y,
      decision_boundary_color,
      train_x,
      train_y,
      train_color,
      test_x,
      test_y,
      test_color,
  ):
      x_dict, y_dict, color_dict = [], [], []
      for i in range(min(len(decision_boundary_x), 100)):
          x_dict.append(decision_boundary_x[i])
          y_dict.append(decision_boundary_y[i])
          color_dict.append(decision_boundary_color)
      for i in range(300):
          x_dict.append(test_x[i])
          y_dict.append(test_y[i])
          color_dict.append(test_color[i])
      for i in range(min(len(train_x), 600)):
          x_dict.append(train_x[i])
          y_dict.append(train_y[i])
          color_dict.append(train_color[i])
  
      return wandb.visualize(
          "wandb/decision_boundaries/v1",
          wandb.Table(
              columns=["x", "y", "color"],
              data=[
                  [x_dict[i], y_dict[i], color_dict[i]]
                  for i in range(len(x_dict))
              ],
          ),
      )
  
label_desc: Use image node_id 1 for calling a global function with example usage: decision_boundaries(decision_boundary_x, decision_boundary_y, decision_boundary_color, train_x, train_y, train_color, test_x, test_y, test_color) and returns: wandb