code: def test_metric_direction_inference():
      # Test min metrics.
      assert metrics_tracking.infer_metric_direction("MAE") == "min"
      assert (
          metrics_tracking.infer_metric_direction(
              metrics.binary_crossentropy
          )
          == "min"
      )
      assert (
          metrics_tracking.infer_metric_direction(
              metrics.FalsePositives()
          )
          == "min"
      )
  
      # All losses in keras.losses are considered as 'min'.
      assert (
          metrics_tracking.infer_metric_direction("squared_hinge")
          == "min"
      )
      assert (
          metrics_tracking.infer_metric_direction(losses.hinge) == "min"
      )
      assert (
          metrics_tracking.infer_metric_direction(
              losses.CategoricalCrossentropy()
          )
          == "min"
      )
  
      # Test max metrics.
      assert (
          metrics_tracking.infer_metric_direction("binary_accuracy")
          == "max"
      )
      assert (
          metrics_tracking.infer_metric_direction(
              metrics.categorical_accuracy
          )
          == "max"
      )
      assert (
          metrics_tracking.infer_metric_direction(metrics.Precision())
          == "max"
      )
  
      # Test unknown metrics.
      assert (
          metrics_tracking.infer_metric_direction("my_metric") is None
      )
  
      def my_metric_fn(x, y):
          return x
  
      assert (
          metrics_tracking.infer_metric_direction(my_metric_fn) is None
      )
  
      class MyMetric(metrics.Metric):
          def update_state(self, x, y):
              return 1
  
          def result(self):
              return 1
  
      assert metrics_tracking.infer_metric_direction(MyMetric()) is None
  
      # Test special cases.
      assert metrics_tracking.infer_metric_direction("loss") == "min"
      assert metrics_tracking.infer_metric_direction("acc") == "max"
      assert metrics_tracking.infer_metric_direction("val_acc") == "max"
      assert (
          metrics_tracking.infer_metric_direction("crossentropy")
          == "min"
      )
      assert metrics_tracking.infer_metric_direction("ce") == "min"
      assert (
          metrics_tracking.infer_metric_direction("weighted_acc")
          == "max"
      )
      assert (
          metrics_tracking.infer_metric_direction("val_weighted_ce")
          == "min"
      )
      assert (
          metrics_tracking.infer_metric_direction(
              "weighted_binary_accuracy"
          )
          == "max"
      )
  
imports: import random
  import numpy
  import pytest
  from keras_tuner.backend.keras import losses
  from keras_tuner.backend.keras import metrics
  from keras_tuner.engine import metrics_tracking
label_desc: Use image node_id 16 for calling a global function with example usage: test_metric_direction_inference() and returns: x, int, int