for (int i = 0; i < b.length; i++) {
            b[i] = (byte) (i + 1);
        }

        // Act: release should zero immediately to avoid leaks
        impl.releaseBuffer(b);

        // Assert: original array is zeroed in-place
        for (byte value : b) {
            assertEquals(0, value, "Released buffer must be zeroed");
        }

        // Next get should return the same instance from cache

This container branches off from
/tensorflow/tools/tf_sig_build_dockerfiles/. However, since
hermetic CUDA and hermetic Python is now available for Tensorflow, a lot of the
requirements installed on the original container can be removed to reduce the
footprint of the container and make it more reusable across different ML

         *
         * 2a. If the resolved types are identical to the original types, then we can return the
         * original, identical JDK TypeVariable. By doing so, we sidestep the problem entirely.
         *
         * 2b. If the resolved types are different from the original types, things are trickier. The

      tensorflow::GetBuildXlaOpsPassFlags();
  bool original = flags->tf_xla_enable_lazy_compilation;
  flags->tf_xla_enable_lazy_compilation = enable;
  return original;
}

unsigned char TF_SetTfXlaCpuGlobalJit(unsigned char enable) {
  tensorflow::MarkForCompilationPassFlags* flags =
      tensorflow::GetMarkForCompilationPassFlags();
  bool original = flags->tf_xla_cpu_global_jit;

  @Override
  public SampleElements<Object> samples() {
    return new Colliders();
  }

  @Override
  public Object[] createArray(int length) {
    return new Object[length];
  }

  /** Returns the original element list, unchanged. */
  @Override
  public List<Object> order(List<Object> insertionOrder) {
    return insertionOrder;
  }

/*
 * Copyright 2019 the original author or authors.
 *
 * Licensed under the Apache License, Version 2.0 (the "License");
 * you may not use this file except in compliance with the License.
 * You may obtain a copy of the License at
 *
 *      http://www.apache.org/licenses/LICENSE-2.0
 *
 * Unless required by applicable law or agreed to in writing, software
 * distributed under the License is distributed on an "AS IS" BASIS,

/*
 * Copyright 2019 the original author or authors.
 *
 * Licensed under the Apache License, Version 2.0 (the "License");
 * you may not use this file except in compliance with the License.
 * You may obtain a copy of the License at
 *
 *      http://www.apache.org/licenses/LICENSE-2.0
 *
 * Unless required by applicable law or agreed to in writing, software
 * distributed under the License is distributed on an "AS IS" BASIS,

    class RoundTripTests {

        @Test
        @DisplayName("Should handle encode-decode round-trip correctly")
        void testEncodeDecodeRoundTrip() throws NdrException {
            // Given: Original value that fits in byte range
            int originalValue = 150;
            NdrShort ndrShort1 = new NdrShort(originalValue);

            // Create separate mocks for encoding and decoding to simulate real buffer

      assertTrue(bf.apply(o));
    }
  }

  public void testCopy() {
    BloomFilter<String> original = BloomFilter.create(Funnels.unencodedCharsFunnel(), 100);
    BloomFilter<String> copy = original.copy();
    assertNotSame(original, copy);
    assertEquals(original, copy);
  }

  public void testExpectedFpp() {
    BloomFilter<Object> bf = BloomFilter.create(HashTestUtils.BAD_FUNNEL, 10, 0.03);

`model.save(f"{fname}.keras", save_format="keras_v3")`. In the future it will become the default for all files with the `.keras` extension. This file format targets the Python runtime only and makes it possible to reload Python objects identical to the saved originals. The format supports non-numerical state such as vocabulary files and lookup tables, and it is easy to customize in the case of custom layers with exotic elements of state (e.g. a FIFOQueue). The format does not rely on bytecode or pickling, and...