}

	for i, tc := range testCases {
		s := Pretty(tc.a)
		if tc.want != s {
			t.Errorf("[%d]:\n\texpected %q\n\tgot      %q", i, tc.want, s)
		}
	}
}

func TestForHash(t *testing.T) {
	tcs := customString("test")
	tpcs := pCustomString("&test")

        }
      },
      "parameters": [
        {
          "description": "If 'true', then the output is pretty printed. Defaults to 'false' unless the user-agent indicates a browser or command-line HTTP tool (curl and wget).",
          "in": "query",
          "name": "pretty",
          "schema": {
            "type": "string",
            "uniqueItems": true
          }
        }
      ],

            "uniqueItems": true
          }
        },
        {
          "description": "If 'true', then the output is pretty printed. Defaults to 'false' unless the user-agent indicates a browser or command-line HTTP tool (curl and wget).",
          "in": "query",
          "name": "pretty",
          "schema": {
            "type": "string",
            "uniqueItems": true
          }
        }
      ],

        }
      },
      "parameters": [
        {
          "description": "If 'true', then the output is pretty printed. Defaults to 'false' unless the user-agent indicates a browser or command-line HTTP tool (curl and wget).",
          "in": "query",
          "name": "pretty",
          "schema": {
            "type": "string",
            "uniqueItems": true
          }
        }
      ],

		}
	}
	size(l.store.Len())
}

func (l *lruCache[K]) ClearAll() {
	l.mu.Lock()
	defer l.mu.Unlock()
	l.token = CacheToken(time.Now().UnixNano())
	// Purge with an evict function would turn up to be pretty slow since
	// it runs the function for every key in the store, might be better to just
	// create a new store.
	l.store = newLru(l.onEvict)
	l.configIndex = map[ConfigHash]sets.Set[K]{}

# RUN: not tf_tfl_translate -mlir-pretty-debuginfo -tf-input-arrays=input -tf-input-data-types=DT_FLOAT -tf-input-shapes=1,224,224,3 -tf-output-arrays=MobilenetV1/MobilenetV1/Conv2d_0/BatchNorm/FusedBatchNorm -tf-debug-info=%s.debug %s -o - 2>&1 | FileCheck %s

# CHECK: fake/user/code/file_C.py:27:1: error: 'tf.Conv2D' op attribute 'data_format' failed to satisfy constraint: 'NHWC' or 'NCHW' convnet data format

node {
  name: "input"
  op: "Placeholder"
  attr {

// traversed field paths. It is necessary to build the tree to take advantage of
// DeepEqual checks performed by lower levels of the object during validation without
// greatly modifying `kube-openapi`'s implementation.
//
// The tree, and all cache storage/scratch space for the validation of a single
// call to `Validate` is thrown away at the end of the top-level call
// to `Validate`.
//

   * A thread-safe cache that contains the mapping from each class to all methods in that class and
   * all super-classes, that are annotated with {@code @Subscribe}. The cache is shared across all
   * instances of this class; this greatly improves performance if multiple EventBus instances are
   * created and objects of the same class are registered on all of them.
   */
  private static final LoadingCache<Class<?>, ImmutableList<Method>> subscriberMethodsCache =

        }

        @Override
        public ImmutableAttributes getFrom() {
            return from;
        }

        @Override
        public ImmutableAttributes getTo() {
            return to;
        }

        @Override
        public TransformStep getTransformStep() {
            return transformStep;
        }

        @Override

docker buildx use container-builder`).Run()
}

// ConstructBakeFile constructs a docker-bake.json to be passed to `docker buildx bake`.
// This command is an extremely powerful command to build many images in parallel, but is pretty undocumented.
// Most info can be found from the source at https://github.com/docker/buildx/blob/master/bake/bake.go.
func ConstructBakeFile(a Args) (map[string]string, error) {