// it's shared to avoid using too much memory in tests
    boolean[] sharedArray = new boolean[arraysDim2];
    Arrays.fill(arrays, sharedArray);

    try {
      Booleans.concat(arrays);
      fail();
    } catch (IllegalArgumentException expected) {
    }
  }

  public void testEnsureCapacity() {
    assertThat(Booleans.ensureCapacity(EMPTY, 0, 1)).isSameInstanceAs(EMPTY);

   * Returns a comparator that compares two {@code char} arrays <a
   * href="http://en.wikipedia.org/wiki/Lexicographical_order">lexicographically</a>; not advisable
   * for sorting user-visible strings as the ordering may not match the conventions of the user's
   * locale. That is, it compares, using {@link #compare(char, char)}), the first pair of values
   * that follow any common prefix, or when one array is a prefix of the other, treats the shorter

      // TODO(lowasser): could be potentially optimized, but only with
      // extensive testing
      try {
        return Float.parseFloat(string);
      } catch (NumberFormatException e) {
        // Float.parseFloat has changed specs several times, so fall through
        // gracefully
      }
    }
    return null;
  }

    /**
     * Starting position of the SMB header in the buffer.
     */
    protected int headerStart;
    /**
     * Total length of the SMB message.
     */
    protected int length;
    /**
     * Batch level for batched requests.
     */
    protected int batchLevel;
    /**
     * Error code returned by the server.
     */
    protected int errorCode;
    /**
     * Additional flags for the SMB message.

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}.

    // 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);
  }

      }
      builder.put(key, valueSet);
      tmpSize += valueCount;
    }

    ImmutableMap<Object, ImmutableSet<Object>> tmpMap;
    try {
      tmpMap = builder.buildOrThrow();
    } catch (IllegalArgumentException e) {
      throw (InvalidObjectException) new InvalidObjectException(e.getMessage()).initCause(e);
    }

    FieldSettersHolder.MAP_FIELD_SETTER.set(this, tmpMap);

    // it's shared to avoid using too much memory in tests
    long[] sharedArray = new long[arraysDim2];
    Arrays.fill(arrays, sharedArray);

    try {
      Longs.concat(arrays);
      fail();
    } catch (IllegalArgumentException expected) {
    }
  }

  private static void assertByteArrayEquals(byte[] expected, byte[] actual) {
    assertWithMessage(

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) {

	"strings"
	"testing"
	"time"
)

type testError struct{ error }

type fileOps []any // []T where T is (string | int64)

// testFile is an io.ReadWriteSeeker where the IO operations performed
// on it must match the list of operations in ops.
type testFile struct {
	ops fileOps
	pos int64
}

func (f *testFile) Read(b []byte) (int, error) {
	if len(b) == 0 {
		return 0, nil
	}
	if len(f.ops) == 0 {