Consider an application streaming a video over http/2. Perhaps the user pauses the video and the application stops reading bytes from this stream. The buffer will fill up, and flow control prevents the server from sending more data on this stream. When the user unpauses her video the buffer drains, the read is acknowledged, and the server proceeds to stream data.

#### Shared reader thread

   * values are null. This method may update {@code alternatingKeysAndValues} if there are duplicate
   * keys. If so, the return value will indicate how many entries are still valid, and will also
   * include a {@link Builder.DuplicateKey} in case duplicate keys are not allowed now or will not
   * be allowed on a later {@link Builder#buildOrThrow()} call.
   *

        assertArrayEquals(plaintext, decrypted1);
        assertArrayEquals(plaintext, decrypted2);

        // Clean up
        Arrays.fill(plaintext, '\0');
        Arrays.fill(decrypted1, '\0');
        Arrays.fill(decrypted2, '\0');
    }

    @Test
    public void testBase64StringOperations() throws Exception {
        char[] plaintext = "TestPassword456".toCharArray();

# Requirements for NumPy 1.x
numpy ~= 1.26.0
wheel ~= 0.41.2
h5py >= 3.11.0, < 3.15.0
lit ~= 17.0.2
opt_einsum == 3.3.0
astunparse == 1.6.3
dill == 0.3.7
astor == 0.7.1
typing_extensions ~= 4.14.1
gast == 0.4.0
termcolor == 2.3.0
wrapt == 1.16.0
tblib == 2.0.0
ml_dtypes >= 0.5.1, < 0.6.0
auditwheel >= 6.1.0
# Install tensorboard, and keras
# Note that here we want the latest version that matches TF major.minor version

numpy>=2.2.6 ; python_version == "3.14"
wheel ~= 0.41.2
h5py >= 3.11.0, < 3.15.0 ; python_version <= "3.13"
h5py ~= 3.15.1 ; python_version == "3.14"
lit ~= 17.0.2
opt_einsum == 3.3.0
astunparse == 1.6.3
dill == 0.3.7
astor == 0.7.1
typing_extensions ~= 4.14.1
gast == 0.4.0
termcolor == 2.3.0
wrapt == 1.16.0
tblib == 2.0.0
ml_dtypes >= 0.5.4, < 0.6.0
auditwheel >= 6.1.0
# Install tensorboard, and keras

        if (this.password != null) {
            // Multi-pass secure wipe of password char array
            Arrays.fill(this.password, '\0');
            Arrays.fill(this.password, '\uFFFF');
            Arrays.fill(this.password, '\uAAAA');
            Arrays.fill(this.password, '\u5555');
            Arrays.fill(this.password, '\0');
            this.password = null;
        }

                                                TF_Status* status);

// This function will block till the operation that produces `h` has
// completed. This is only valid on local TFE_TensorHandles. The pointer
// returned will be on the device in which the TFE_TensorHandle resides (so e.g.
// for a GPU tensor this will return a pointer to GPU memory). The pointer is

 * acquire(1000)} will result in exactly the same throttling, if any), but it affects the throttling
 * of the <i>next</i> request. I.e., if an expensive task arrives at an idle RateLimiter, it will be
 * granted immediately, but it is the <i>next</i> request that will experience extra throttling,
 * thus paying for the cost of the expensive task.
 *

                    Then, run `:checkTargetRuntimes --fix`.

                    ```
                    dependencies {
                        // ...
                    }

                    gradleModule {
                        requiredRuntimes {
                            // Fill in this block as required
                        }

        By asking questions in a structured way (following this) it will be much easier to help you.

        And there's a high chance that you will find the solution along the way and you won't even have to submit it and wait for an answer. 😎