/*
 * Licensed to the Apache Software Foundation (ASF) under one
 * or more contributor license agreements.  See the NOTICE file
 * distributed with this work for additional information
 * regarding copyright ownership.  The ASF licenses this file
 * to you 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
 *

        source = Buffer(),
      )

    assertFailsWith<EOFException> {
      parts.nextPart()
    }
  }

  /** Confirm that [MultipartBody] and [MultipartReader] can work together. */
  @Test fun `multipart round trip`() {
    val body =
      MultipartBody
        .Builder("boundary")
        .setType(MultipartBody.PARALLEL)

    So, it is a generator function that transfers the "generating" work to something else internally.

    By doing it this way, we can put it in a `with` block, and that way, ensure that the file-like object is closed after finishing.

/// tip

/*
 * Licensed to the Apache Software Foundation (ASF) under one
 * or more contributor license agreements.  See the NOTICE file
 * distributed with this work for additional information
 * regarding copyright ownership.  The ASF licenses this file
 * to you 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
 *

   *   <li>{@code consistentHash(h, n - 1)}, otherwise (probability {@code 1 - 1/n})
   * </ul>
   *
   * <p>This method is suitable for the common use case of dividing work among buckets that meet the
   * following conditions:
   *
   * <ul>
   *   <li>You want to assign the same fraction of inputs to each bucket.

    visibility = ["//visibility:public"],
)

# DO NOT ADD ANY NEW EXCEPTIONS TO THIS LIST!
# Instead, please use public APIs or public build rules TF provides.
# If you need functionality that is not exposed, we will work with you to expand our public APIs.
# TODO(b/173549186): Move Google-internal TF code out of learning/brain
package_group(
    name = "internal",
    packages = [
        "//devtools/python/indexer/...",

    *   Extend scatter operations to work with a scalar update parameter.
    *   Move cuDNN RNN ops to core for use in TensorFlow codebase only.
    *   Add `float64` support for `Conv2d`, `Conv2dBackpropInput`, and
        `Conv2dBackpropFilter`.
    *   Add `float64` support for `AvgPool`/`AvgPoolGrad`.
    *   Make graph name scope thread local so that they work correctly in
        multi-threaded environments.

        // When - Set very low rotation limits to trigger rotation
        context.setKeyRotationBytesLimit(100); // Rotate after 100 bytes

        // Encrypt first message - should work
        byte[] plaintext1 = "Message before rotation".getBytes();
        byte[] encrypted1 = assertDoesNotThrow(() -> context.encryptMessage(plaintext1, 1L));
        assertNotNull(encrypted1);

          <show>package</show>
        </configuration>
      </plugin>
      <!-- Disable "normal" testing, which doesn't work for GWT tests. -->
      <plugin>
        <artifactId>maven-surefire-plugin</artifactId>
        <configuration>
          <skip>true</skip>
        </configuration>
      </plugin>
      <plugin>

        assertEquals(0, result);
    }

    @ParameterizedTest
    @ValueSource(ints = { 0, 10, 50, 100 })
    @DisplayName("writeBytesWireFormat should work at different offsets")
    void testWriteBytesWireFormatAtDifferentOffsets(int offset) {
        byte[] buffer = new byte[512];

        int written = request.writeBytesWireFormat(buffer, offset);