var buf bytes.Buffer
	tw := NewWriter(&buf)
	if err := tw.AddFS(fsys); err != nil {
		t.Fatal(err)
	}
	if err := tw.Close(); err != nil {
		t.Fatal(err)
	}

	// Add subfolder into fsys to match what we'll read from the tar.
	fsys["subfolder"] = &fstest.MapFile{Mode: 0o555 | os.ModeDir}

	// Test that we can get the files back from the archive
	tr := NewReader(&buf)

your changes by running the command:

```bash
tensorflow/tools/ci_build/ci_build.sh CPU tensorflow/tools/ci_build/ci_sanity.sh
```

This will catch most license, Python coding style and BUILD file issues that
may exist in your changes.

#### Running unit tests

There are two ways to run TensorFlow unit tests.

<pre class="grammar">
Import declaration          Local name of Sin

import   "lib/math"         math.Sin
import m "lib/math"         m.Sin
import . "lib/math"         Sin
</pre>

<p>
An import declaration declares a dependency relation between
the importing and imported package.
It is illegal for a package to import itself, directly or indirectly,
or to directly import a package without

	var testTime = time.Date(2009, time.November, 10, 23, 45, 58, 0, time.UTC)
	fh := new(FileHeader)
	fh.SetModTime(testTime)
	outTime := fh.ModTime()
	if !outTime.Equal(testTime) {
		t.Errorf("times don't match: got %s, want %s", outTime, testTime)
	}
}

func testHeaderRoundTrip(fh *FileHeader, wantUncompressedSize uint32, wantUncompressedSize64 uint64, t *testing.T) {
	fi := fh.FileInfo()
	fh2, err := FileInfoHeader(fi)

func init() {
	testBytes = make([]byte, N)
	for i := 0; i < N; i++ {
		testBytes[i] = 'a' + byte(i%26)
	}
	testString = string(testBytes)
}

// Verify that contents of buf match the string s.
func check(t *testing.T, testname string, buf *Buffer, s string) {
	bytes := buf.Bytes()
	str := buf.String()
	if buf.Len() != len(bytes) {

	}, {
		in: []sparseEntry{{0, 0}}, size: -100,
		wantValid: false,
	}, {
		in: []sparseEntry{{math.MaxInt64, 3}, {6, -5}}, size: 35,
		wantValid: false,
	}, {
		in: []sparseEntry{{1, 3}, {6, -5}}, size: 35,
		wantValid: false,
	}, {
		in: []sparseEntry{{math.MaxInt64, math.MaxInt64}}, size: math.MaxInt64,
		wantValid: false,
	}, {
		in: []sparseEntry{{3, 3}}, size: 5,
		wantValid: false,
	}, {

	n, err := tw.curr.Write(b)
	if err != nil && err != ErrWriteTooLong {
		tw.err = err
	}
	return n, err
}

// readFrom populates the content of the current file by reading from r.
// The bytes read must match the number of remaining bytes in the current file.
//
// If the current file is sparse and r is an io.ReadSeeker,
// then readFrom uses Seek to skip past holes defined in Header.SparseHoles,

    // Compare that the graphs match.
    GraphDef expected_gdef;
    GraphDef gdef;
    EXPECT_TRUE(GetGraphDef(expected_graph_, &expected_gdef));
    EXPECT_TRUE(GetGraphDef(graph_, &gdef));
    TF_EXPECT_GRAPH_EQ(expected_gdef, gdef);

    // Compare that the output of the gradients of both graphs match.
    RunGraphsAndCompareOutputs(grad_outputs, expected_grad_outputs);
  }

That, plus the simple fact that Python is the main language for **Data Science**, Machine Learning and especially Deep Learning, make FastAPI a very good match for Data Science / Machine Learning web APIs and applications (among many others).

To see how to achieve this parallelism in production see the section about [Deployment](deployment/index.md){.internal-link target=_blank}.

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