}
}

// Runs model as is if output is a scalar,
// else sums the output tensor before returning.
Status RunAndMaybeSum(AbstractContext* ctx, Model forward,
                      absl::Span<AbstractTensorHandle* const> inputs,
                      absl::Span<AbstractTensorHandle*> outputs,
                      bool use_function) {
  AbstractTensorHandle* model_outputs[1];

  // Run the model.
  TF_RETURN_IF_ERROR(

```

👆 💪 📨 & 📨 💱, ✍, & 🎻 💽.

## 🔄 ⚫️

🚥 👆 📁 📛 `main.py`, 🏃 👆 🈸 ⏮️:

<div class="termy">

```console
$ uvicorn main:app --reload

<span style="color: green;">INFO</span>:     Uvicorn running on http://127.0.0.1:8000 (Press CTRL+C to quit)
```

</div>

📂 👆 🖥 <a href="http://127.0.0.1:8000" class="external-link" target="_blank">http://127.0.0.1:8000</a>.

  std::optional<AttrKeyValuePair> attr_;
};

::testing::Matcher<const Node*> NodeWith(
    absl::Span<const NodeMatcherProperties> props);

impl::NodeMatcherProperties Inputs(
    absl::Span<const ::testing::Matcher<OutEdge>> inputs);

impl::NodeMatcherProperties CtrlDeps(
    absl::Span<const ::testing::Matcher<const Node*>> control_deps);

impl::NodeMatcherProperties Attr(std::pair<string, AttrValue> attrs);

    params->emplace_back(handle);
  }
  return absl::OkStatus();
}

// Runs `model` maybe wrapped in a function.
Status RunModel(Model model, AbstractContext* ctx,
                absl::Span<AbstractTensorHandle* const> inputs,
                absl::Span<AbstractTensorHandle*> outputs, bool use_function) {
  if (use_function) {
    const char* fn_name = "test_fn";
    core::RefCountPtr<AbstractFunction> scoped_func;

			continue
		}
		obj, span, objIndex := findObject(ptr, 0, 0)
		if obj == 0 {
			continue
		}
		// TODO: Consider making two passes where the first
		// just prefetches the mark bits.
		mbits := span.markBitsForIndex(objIndex)
		if mbits.isMarked() {
			continue
		}
		mbits.setMarked()

		// Mark span.
		arena, pageIdx, pageMask := pageIndexOf(span.base())

  // XlaCompilationResult pointed to by `result`.
  virtual Status Compile(const XlaCompiler::CompileOptions& options,
                         const NameAttrList& function,
                         absl::Span<const XlaArgument> args,
                         XlaCompilationResult* result) = 0;

  // Compiles a Tensorflow single op to an HloModuleProto stored in the
  // XlaCompilationResult pointed to by `result`.

    <a class="badge" href="https://dpeuniversity.gradle.com/app/courses/ec69d0b8-9171-4969-ac3e-82dea16f87b0/" target="_blank">
        <span class="badge-type button--blue">LEARN</span>
        <span class="badge-text">Incremental Builds and Build Caching with Gradle&nbsp;&nbsp;&nbsp;&gt;</span>
    </a>
</div>
++++

Gradle uses two main features to reduce build time: *incremental builds* and *build caching*.

    <a class="badge" href="https://dpeuniversity.gradle.com/app/courses/ec69d0b8-9171-4969-ac3e-82dea16f87b0/" target="_blank">
        <span class="badge-type button--blue">LEARN</span>
        <span class="badge-text">Incremental Builds and Build Caching with Gradle&nbsp;&nbsp;&nbsp;&gt;</span>
    </a>
</div>
++++

Learn the basics of Gradle's incremental builds.

****
**In this section you will:**

 *
 * See LoggingBuildOperationProgressBroadcaster.
 *
 * @since 7.4
 */
public interface StyledTextBuildOperationProgressDetails {

    List<? extends Span> getSpans();

    String getCategory();

    LogEventLevel getLevel();

    interface Span {

        /**
         * Always a value name of {@code org.gradle.internal.logging.text.StyledTextOutput.Style}.
         */
        String getStyleName();

                                     absl::Span<const XlaArgument> args,
                                     XlaCompilationResult* result) {
  return ADD_SOURCE_LOCATION(
      xla_compiler_.CompileFunction(options, function, args, result));
}

Status TfGraphToHloCompiler::CompileSingleOp(
    const XlaCompiler::CompileOptions& options, const OpKernelContext* ctx,
    absl::Span<const XlaArgument> args, XlaCompilationResult* result) {