- type: input
    id: Mobile
    attributes:
      label: Mobile device
      placeholder: e.g., Linux Ubuntu 16.04
  - type: input
    id: Python
    attributes:
      label: Python version
      placeholder: e.g., 3.9
  - type: input
    id: Bazel
    attributes:
      label: Bazel version
      description: If compiling from source
  - type: input
    id: Compiler
    attributes:

  private static final Predicate<@Nullable String> NOT_LENGTH_3 =
      input -> input == null || input.length() != 3;
  private static final Predicate<@Nullable Integer> EVEN = input -> input == null || input % 2 == 0;
  static final Predicate<Entry<String, Integer>> CORRECT_LENGTH =
      input -> input.getKey().length() == input.getValue();

  abstract Map<String, Integer> createUnfiltered();

  private static final Predicate<@Nullable String> NOT_LENGTH_3 =
      input -> input == null || input.length() != 3;
  private static final Predicate<@Nullable Integer> EVEN = input -> input == null || input % 2 == 0;
  static final Predicate<Entry<String, Integer>> CORRECT_LENGTH =
      input -> input.getKey().length() == input.getValue();

  abstract Map<String, Integer> createUnfiltered();

    }

    @get:Input
    abstract val version: Property<String>

    @get:Input
    abstract val baseVersion: Property<String>

    @get:Input
    @get:Optional
    abstract val commitId: Property<String>

    @get:Input
    abstract val snapshot: Property<Boolean>

    @get:Input
    abstract val promotionBuild: Property<Boolean>

    @get:Input
    @get:Optional

// Creates parameters (placeholders) in the tracing `ctx` using the shape and
// dtype of `inputs`.
Status CreateParamsForInputs(AbstractContext* ctx,
                             absl::Span<AbstractTensorHandle* const> inputs,
                             std::vector<AbstractTensorHandle*>* params);

// A callable that takes tensor inputs and returns zero or more tensor outputs.
using Model = std::function<Status(AbstractContext*,

}
Status TapeOperation::AddInput(AbstractTensorHandle* input) {
  TF_RETURN_IF_ERROR(parent_op_->AddInput(input));
  forward_op_.inputs.push_back(input);
  return OkStatus();
}
Status TapeOperation::AddInputList(
    absl::Span<AbstractTensorHandle* const> inputs) {
  TF_RETURN_IF_ERROR(parent_op_->AddInputList(inputs));
  for (auto input : inputs) {
    forward_op_.inputs.push_back(input);
  }
  return OkStatus();
}

  static <V extends @Nullable Object, X extends Throwable> ListenableFuture<V> create(
      ListenableFuture<? extends V> input,
      Class<X> exceptionType,
      Function<? super X, ? extends V> fallback,
      Executor executor) {
    CatchingFuture<V, X> future = new CatchingFuture<>(input, exceptionType, fallback);
    input.addListener(future, rejectionPropagatingExecutor(executor, future));
    return future;
  }

					<div class="input-group mb-3">
						<c:set var="ph_new_password">
							<la:message key="labels.profile.placeholder_new_password" />
						</c:set>
						<la:password property="newPassword" class="form-control"
									 placeholder="${ph_new_password}" />
						<div class="input-group-append">
							<span class="input-group-text"><em class="fa fa-lock fa-fw"></span>
						</div>
					</div>
					<div class="input-group mb-3">

    // the output, so the input must be at least 32 bits, since the output has to be that long
    assertPutString(new char[] {'p', HashTestUtils.randomLowSurrogate(new Random())});
  }

  public void testPutStringWithHighSurrogate() {
    // we pad because the dummy hash function we use to test this, merely copies the input into

    // the output, so the input must be at least 32 bits, since the output has to be that long
    assertPutString(new char[] {'p', HashTestUtils.randomLowSurrogate(new Random())});
  }

  public void testPutStringWithHighSurrogate() {
    // we pad because the dummy hash function we use to test this, merely copies the input into