Entry<K, V> entry = iterator.next();
      Entry<K, V> entryCopy = mapEntry(entry.getKey(), entry.getValue());

      iterator.remove();
      assertEquals(initialSize - 1, map.size());

      // Use "entryCopy" instead of "entry" because "entry" might be invalidated after
      // iterator.remove().
      assertFalse(entrySet.contains(entryCopy));
      assertInvariants(map);

      Entry<K, V> entry = iterator.next();
      Entry<K, V> entryCopy = mapEntry(entry.getKey(), entry.getValue());

      iterator.remove();
      assertEquals(initialSize - 1, map.size());

      // Use "entryCopy" instead of "entry" because "entry" might be invalidated after
      // iterator.remove().
      assertFalse(entrySet.contains(entryCopy));
      assertInvariants(map);

                .build();
        ActivationOS ao = ActivationOS.newBuilder()
                .arch("arch" + Long.toHexString(entropy.nextLong()))
                .family("family" + Long.toHexString(entropy.nextLong()))
                .name("name" + Long.toHexString(entropy.nextLong()))
                .version("version" + Long.toHexString(entropy.nextLong()))
                .build();

        }

        // Simple heuristic based on data entropy
        // In practice, this would be more sophisticated
        int uniqueBytes = countUniqueBytes(data);
        double entropy = (double) uniqueBytes / 256.0;

        // Estimate compression ratio based on entropy
        // Lower entropy = better compression
        return 0.3 + (entropy * 0.6); // Range from 30% to 90%
    }

    @Override

            uniqueCounters.add(bytesToHex(counter));
        }

        // All nonces should be unique (high entropy from SecureRandom)
        assertEquals(sampleSize, uniqueCounters.size(), "All nonces should have unique values");

        // Verify that nonces have good entropy (not predictable)
        // With SecureRandom + counter XOR, they should all be different
        Set<String> uniqueNonces = new HashSet<>();

        `tf.initializers = tf.keras.initializers` & `tf.optimizers =
        tf.keras.optimizers`.
    *   Updates binary cross entropy logic in Keras when input is probabilities.
        Instead of converting probabilities to logits, we are using the cross
        entropy formula for probabilities.
    *   Added public APIs for `cumsum` and `cumprod` keras backend functions.

system's random number generator. package drbg import ( entropy "crypto/internal/entropy/v1.0.0" "crypto/internal/fips140" "crypto/internal/sysrand" "io" "sync" "sync/atomic" ) // memory is a scratch buffer that is accessed between samples by the entropy // source to expose it to memory access timings. // // We reuse it and share it between Seed calls to avoid the significant (~500µs) // cost of zeroing a new allocation every time. The entropy source accesses it // using atomics (and doesn't care about...