TF_DeleteTensor(tensor);
  TFE_DeleteTensorHandle(tensor_handle);

  std::vector<int64_t> values(4, 1);
  TFE_OpSetAttrIntList(op, "ksize", values.data(), values.size());
  TFE_OpSetAttrIntList(op, "strides", values.data(), values.size());

  const int BUFFER_SIZE = 10;
  char buffer[BUFFER_SIZE];
  std::strncpy(buffer, "VALID", BUFFER_SIZE);

                do {
                    long ooff = 0;
                    while (ooff < size) {
                        long wsize = size - ooff;
                        if (wsize > byteLimit) {
                            wsize = byteLimit;
                        }

                        int chunks = (int) (wsize / maxChunkSize);
                        int lastChunkSize;

Ian Lance Taylor <******@****.***> 1765577893 -0800

	decryptedRangeRef := func(s []int64, skipLen, readLen int64, isFromEnd bool) (o, l, skip int64, sn uint32, ps int) {
		oSize := lsum(s)
		if isFromEnd {
			skipLen = oSize - readLen
		}
		if skipLen < 0 || readLen < 0 || oSize < 0 || skipLen+readLen > oSize {
			t.Fatalf("Impossible read specified: %d %d %d", skipLen, readLen, oSize)
		}

		var cumulativeSum, cumulativeEncSum int64
		toRead := readLen
		readStart := false

		if text == nil {
			t.Errorf("%s: instruction outside TEXT", p)
		}
		size := int64(len(text.P)) - p.Pc
		if p.Link != nil {
			size = p.Link.Pc - p.Pc
		} else if p.Isize != 0 {
			size = int64(p.Isize)
		}
		var code []byte
		if p.Pc < int64(len(text.P)) {
			code = text.P[p.Pc:]
			if size < int64(len(code)) {
				code = code[:size]
			}
		}
		codeHex := fmt.Sprintf("%x", code)
		if codeHex == "" {

            try {
                w.start();
                // use commonly acceptable buffer size
                final int bsize = Math.min(sh.getReceiveBufferSize() - 70, dh.getSendBufferSize() - 70);
                final byte[][] b = new byte[2][bsize];
                copyRecursive(dest, b, bsize, w, sh, dh);
            } finally {
                w.write(null, -1, null);
                w.interrupt();

            t2.snd_buf_size = t1.snd_buf_size;
        } else {
            t1.snd_buf_size = t2.snd_buf_size;
        }

        bsize = Math.min(t1.rcv_buf_size - 70, t1.snd_buf_size - 70);
        b = new byte[2][bsize];

        try {
            copyTo0(dest, b, bsize, w, req, resp);
        } finally {
            w.write(null, -1, null, 0);
        }
    }

    /**

  size_t size = results.missing_unused_input_map_keys.size();
  tf_results->missing_unused_key_names.resize(size);
  tf_results->missing_unused_key_indexes.resize(size);

  for (int i = 0; i < size; ++i) {
    TensorId id = results.missing_unused_input_map_keys[i];
    tf_results->missing_unused_key_names_data.emplace_back(id.first);
    tf_results->missing_unused_key_names[i] =

            throw new IllegalArgumentException(
                    "progressed file size cannot be greater than size: " + progressedSize + " > " + size);
        }

        if (size >= 0L && progressedSize != size) {
            ScaleUnit unit = ScaleUnit.getScaleUnit(size);
            format(builder, progressedSize, unit, true);
            builder.append("/");
            format(builder, size, unit, false);

    }
    return dummy;
  }

  @Benchmark
  int preSizedBuilderAdd(int reps) {
    int size = this.size;
    int dummy = 0;
    for (int rep = 0; rep < reps; rep++) {
      ImmutableList.Builder<Object> builder = new ImmutableList.Builder<>(size);
      for (int i = 0; i < size; i++) {
        builder.add(OBJECT);
      }
      dummy += builder.build().size();
    }
    return dummy;
  }

  @Benchmark