// 1) nullptr indicates tensor is compact and row-majored.
  // 2) fill in the strides array as the real case for compact row-major data.
  // Here we choose option 2, since some frameworks didn't handle the strides
  // argument properly.
  dlm_tensor->dl_tensor.strides = stride_arr->data();

  dlm_tensor->dl_tensor.byte_offset =
      0;  // TF doesn't handle the strides and byte_offsets here
  return static_cast<void*>(dlm_tensor);
}

    } else {
      return next >>> 1;
    }
  }

  public void testCrc32cStrideTable() {
    int next = CRC32C_GENERATOR_FLIPPED;
    for (int i = 0; i < 12; i++) { // for 3 ints = 12 bytes in between each stride window
      next = (next >>> 8) ^ Crc32cHashFunction.Crc32cHasher.BYTE_TABLE[next & 0xFF];
    }
    int[][] expected = new int[4][256];
    for (int b = 0; b < 4; ++b) {
      for (int bit = 128; bit != 0; bit >>= 1) {

	}
	return
}

func testReadLine(t *testing.T, input []byte) {
	for stride := 1; stride < 2; stride++ {
		done := 0
		reader := testReader{input, stride}
		l := NewReaderSize(&reader, len(input)+1)
		for {
			line, isPrefix, err := l.ReadLine()
			if len(line) > 0 && err != nil {
				t.Errorf("ReadLine returned both data and error: %s", err)
			}
			if isPrefix {
				t.Errorf("ReadLine returned prefix")

    } else {
      return next >>> 1;
    }
  }

  public void testCrc32cStrideTable() {
    int next = CRC32C_GENERATOR_FLIPPED;
    for (int i = 0; i < 12; i++) { // for 3 ints = 12 bytes in between each stride window
      next = (next >>> 8) ^ Crc32cHashFunction.Crc32cHasher.BYTE_TABLE[next & 0xFF];
    }
    int[][] expected = new int[4][256];
    for (int b = 0; b < 4; ++b) {
      for (int bit = 128; bit != 0; bit >>= 1) {

	var rs []rune
	for _, r16 := range rt.R16 {
		for r := rune(r16.Lo); r <= rune(r16.Hi); r += rune(r16.Stride) {
			if r != needle {
				rs = append(rs, rune(r))
			}
		}
	}
	for _, r32 := range rt.R32 {
		for r := rune(r32.Lo); r <= rune(r32.Hi); r += rune(r32.Stride) {
			if r != needle {
				rs = append(rs, rune(r))
			}
		}
	}

*   Adds FULLY_CONNECTED Op to tensorflow/lite/schema.fbs

## Known Issues

*   Tensorflow_gpu compilation fails with Bazel 0.5.3.

## Bug Fixes and Other Changes

*   Fixes `strides` and `begin` dtype mismatch when slicing using int64 Tensor
    index in python.
*   Improved convolution padding documentation.
*   Add a tag constant, gpu, to present graph with GPU support.