* tests are divided into three sections:
   * 1. Tests on a hardcoded dataset for chains starting with median(), quartiles(), and scale(10);
   * 2. Tests on hardcoded datasets include non-finite values for chains starting with scale(10);
   * 3. Tests on a mechanically generated dataset for chains starting with percentiles();
   * 4. Tests of illegal usages of the API.
   */

  /*

  public static LinearTransformation horizontal(double y) {
    checkArgument(isFinite(y));
    double slope = 0.0;
    return new RegularLinearTransformation(slope, y);
  }

  /**
   * Builds an instance for datasets which contains {@link Double#NaN}. The {@link #isHorizontal}
   * and {@link #isVertical} methods return {@code false} and the {@link #slope}, and {@link

        assertThrows(CIFSException.class, () -> sidResolver.getServerSid(mockContext, testServerName));
    }

    // Test with large datasets
    @Test
    void testResolveSids_LargeArray() throws CIFSException {
        SID[] largeSidArray = new SID[1000];
        for (int i = 0; i < 1000; i++) {
            largeSidArray[i] = mock(SID.class);
        }

}

// calculateSignedChunkLength - calculates the length of the overall stream (data + metadata)
func calculateStreamContentLength(dataLen, chunkSize int64) int64 {
	if dataLen <= 0 {
		return 0
	}
	chunksCount := dataLen / chunkSize
	remainingBytes := dataLen % chunkSize
	var streamLen int64
	streamLen += chunksCount * calculateSignedChunkLength(chunkSize)
	if remainingBytes > 0 {

    }

    /**
     * Exports all crawling information parameters to CSV format.
     * The CSV output includes: SessionId, SessionCreatedTime, Key, Value, CreatedTime.
     * Uses cursor-based selection to handle large datasets efficiently.
     *
     * @param writer the Writer to output CSV data to
     */
    public void exportCsv(final Writer writer) {
        final CsvConfig cfg = new CsvConfig(',', '"', '"');

		// Use obscured file to avoid Apple’s notarization service
		// rejecting the toolchain due to an inability to unzip this archive.
		// See golang.org/issue/34986
		Name:     "go-no-datadesc-sig.zip.base64",
		Obscured: true,
		File: []ZipTestFile{
			{
				Name:     "foo.txt",
				Content:  []byte("foo\n"),
				Modified: time.Date(2012, 3, 8, 16, 59, 10, 0, timeZone(-8*time.Hour)),

        components.
    *   Improve the performance of datasets using `from_tensors()`.
    *   Promoting `unbatch` from experimental to core API.
    *   Adding support for datasets as inputs to `from_tensors` and
        `from_tensor_slices` and batching and unbatching of nested datasets.

*   `tf.distribute`:

1EE1C         ; mapped                 ; 066E          # 6.1  ARABIC MATHEMATICAL DOTLESS BEH
1EE1D         ; mapped                 ; 06BA          # 6.1  ARABIC MATHEMATICAL DOTLESS NOON
1EE1E         ; mapped                 ; 06A1          # 6.1  ARABIC MATHEMATICAL DOTLESS FEH
1EE1F         ; mapped                 ; 066F          # 6.1  ARABIC MATHEMATICAL DOTLESS QAF
1EE20         ; disallowed                             # NA   <reserved-1EE20>

  EXPECT_EQ(TF_UNAVAILABLE,
            static_cast<TF_Code>(tensorflow::error::UNAVAILABLE));
  EXPECT_EQ(TF_DATA_LOSS, static_cast<TF_Code>(tensorflow::error::DATA_LOSS));
}

TEST(CAPI, GetAllOpList) {
  TF_Buffer* buf = TF_GetAllOpList();
  tensorflow::OpList op_list;
  EXPECT_TRUE(op_list.ParseFromArray(buf->data, buf->length));
  EXPECT_GT(op_list.op_size(), 0);

pkg syscall (freebsd-386-cgo), type BpfHdr struct
pkg syscall (freebsd-386-cgo), type BpfHdr struct, Caplen uint32
pkg syscall (freebsd-386-cgo), type BpfHdr struct, Datalen uint32
pkg syscall (freebsd-386-cgo), type BpfHdr struct, Hdrlen uint16
pkg syscall (freebsd-386-cgo), type BpfHdr struct, Pad_cgo_0 [2]uint8
pkg syscall (freebsd-386-cgo), type BpfHdr struct, Tstamp Timeval