ILIYA <******@****.***> 1748359143 +0900

Klaus Post <******@****.***> 1756435188 +0200

Klaus Post <******@****.***> 1756435188 +0200

     */
    runConcurrentlyMutatedTest(elements(), ops(add(1), add(2)), wrap);

    runConcurrentlyMutatedTest(elements(), ops(add(1), nop()), wrap);

    runConcurrentlyMutatedTest(elements(), ops(add(1), remove()), wrap);

    runConcurrentlyMutatedTest(elements(), ops(nop(), add(1)), wrap);

    runConcurrentlyMutatedTest(elements(1), ops(remove(), nop()), wrap);

	v := &g[T]{
		db:  db,
		ops: make([]op, 0, 5),
	}

	if len(opts) > 0 {
		v.ops = append(v.ops, func(db *DB) *DB {
			return db.Clauses(opts...)
		})
	}

	v.createG = &createG[T]{
		chainG: chainG[T]{
			execG: execG[T]{g: v},
		},
	}
	return v
}

type g[T any] struct {
	*createG[T]
	db  *DB
	ops []op
}

					}
				case testReadFrom:
					f := &testFile{ops: tf.ops}
					got, err := tw.readFrom(f)
					if _, ok := err.(testError); ok {
						t.Errorf("test %d, ReadFrom(): %v", i, err)
					} else if got != tf.wantCnt || !equalError(err, tf.wantErr) {
						t.Errorf("test %d, ReadFrom() = (%d, %v), want (%d, %v)", i, got, err, tf.wantCnt, tf.wantErr)
					}
					if len(f.ops) > 0 {

        double overallTimeMs = (overallEnd - overallStart) / 1_000_000.0;
        double avgThreadTimeMs = totalTime.get() / (threadCount * 1_000_000.0);

        System.out.printf("Connection Pool Performance: %d ops in %.2f ms (%.2f ms avg per thread)%n", successCount.get(), overallTimeMs,
                avgThreadTimeMs);

        // Verify performance improvements

* Add ops to `tensorflow.raw_ops` that were missing.

* `tf.CheckpointOptions`

        "//tensorflow/core/kernels/data:optional_ops_util",
        "//tensorflow/core/platform:abi",
    ]),
    alwayslink = 1,
)

tf_cuda_library(
    name = "ops",
    srcs = [
        "ops.cc",
    ],
    hdrs = [
        "ops.h",
    ],
    copts = tf_copts(),
    visibility = ["//visibility:public"],
    deps = [
        ":c_api_macros_hdrs",
        ":tf_datatype",

# A shared object which includes registration mechanisms for ops and
# kernels. Does not include the implementations of any ops or kernels. Instead,
# the library which loads libtensorflow_framework.so
# (e.g. _pywrap_tensorflow_internal.so for Python, libtensorflow.so for the C
# API) is responsible for registering ops with libtensorflow_framework.so. In
# addition to this core set of ops, user libraries which are loaded (via