import java.io.File;
import java.io.FileNotFoundException;
import java.io.PrintStream;
import java.text.SimpleDateFormat;
import java.util.Date;
import java.util.concurrent.Callable;

/**
 * <p>Stage 2 of the start-up for a worker process with the application classes loaded in the system ClassLoader. Takes
 * care of deserializing and invoking the worker action.</p>
 *

        run "compileGroovy"

        then:
        outputs.recompiledClasses("B", "D")

        where:
        // We must trigger failure in a "Semantic Analysis" stage and not in parse stage, otherwise compiler won't output anything on a disk.
        // So for example "class A { garbage }" will cause compiler to fail very early and nothing will be written on a disk.

    {
        "/tensorflow/cc/saved_model/load_latency_by_stage",  // metric name
        "Distribution of wall time spent (in microseconds) in each stage "
        "(restore graph from disk, run init graph op, etc) when loading the "
        "model",
        "model_path",
        "stage",
    },
    // Scale of 10, power of 1.8 with bucket count 37 (~258 minutes).
    monitoring::Buckets::Exponential(10, 1.8, 37));

# (1)
FROM python:3.9 as requirements-stage

# (2)
WORKDIR /tmp

# (3)
RUN pip install poetry

# (4)
COPY ./pyproject.toml ./poetry.lock* /tmp/

# (5)
RUN poetry export -f requirements.txt --output requirements.txt --without-hashes

# (6)
FROM python:3.9

# (7)
WORKDIR /code

# (8)
COPY --from=requirements-stage /tmp/requirements.txt /code/requirements.txt

	// Run all the require functions first, then the setup functions.
	setupFns := append(append([]resource.SetupFn{}, s.requireFns...), s.setupFns...)

	// don't waste time setting up if already skipped
	if s.isSkipped(ctx) {
		return nil
	}

	start := time.Now()
	for _, fn := range setupFns {
		err := s.runSetupFn(fn, ctx)
		if err != nil {

		auditConfig := evaluator.EvaluatePolicyRule(attrs)
		assert.Equal(t, expected, auditConfig.Level)
		if auditConfig.Level != audit.LevelNone {
			assert.ElementsMatch(t, auditConfig.OmitStages, []audit.Stage{audit.StageRequestReceived})
		}
	})
}

func newUserInfo(name string, groups ...string) user.Info {
	return &user.DefaultInfo{
		Name:   name,
		Groups: groups,
	}
}

### Typical Pull Request Workflow -

**1. New PR**

- As a contributor, you submit a New PR on GitHub.
- We inspect every incoming PR and add certain labels to the PR such as `size:`,
  `comp:` etc.  At this stage we check if the PR is valid and meets certain
  quality requirements. For example, we check if the CLA is signed, PR has
  sufficient description, if applicable unit tests are added, if it is a

    bool is_packed = partitioned_input.getIsPacked();
    const auto func = partitioned_input->getParentOfType<mlir::func::FuncOp>();

    for (Value input : partitioned_input.getInputs()) {
      // At this stage, the op may've been rewritten to be "unpacked" but
      // taking multiple copies of a resource on a COMPOSITE device
      is_packed =
          is_packed || IsCompositeDevice(GetDeviceOfResource(func, input));

    }

    private void ensureEmptyDirectoriesBeforeExecution() {
        try {
            tempDir.mkdirs();

            // Create or clean stash and stage directories
            Set<File> ensureEmptyDirectories = new HashSet<>();
            deleter.ensureEmptyDirectory(stashDirectory);
            ensureEmptyDirectories.add(stashDirectory);

# (1)
FROM python:3.9 as requirements-stage

# (2)
WORKDIR /tmp

# (3)
RUN pip install poetry

# (4)
COPY ./pyproject.toml ./poetry.lock* /tmp/

# (5)
RUN poetry export -f requirements.txt --output requirements.txt --without-hashes

# (6)
FROM python:3.9

# (7)
WORKDIR /code

# (8)
COPY --from=requirements-stage /tmp/requirements.txt /code/requirements.txt