// to TF Functional internally. Individual Op inside a node should be the
// Tensorflow Dialect. The output MLIR is in the TF Functional Dialect.  The
// input MLIR should not have infeed and outfeed ops, which are unsupported via
// this API. Returns OkStatus if passed, otherwise an error.
tensorflow::Status RunSessionTf2xlaClusteringBridge(
    mlir::ModuleOp module, bool is_in_fallback_enabled_mode);

    }
    rewriter.replaceOp(op, ValueRange(results));
    return success();
  }
};

// Converts tf.OutfeedEnqueueTuple to XLA HLO tuple, create_token and outfeed
// ops.
//
// XLA HLO outfeed op expects a token, which we generate by emitting an
// create_token op.
//
// For example the following IR:
// "tf.OutfeedEnqueueTuple"(%val_1, %val_2) : (tensor<3xi32>, tensor<4xf32>) ->

	}
	x := &ofb{
		b:       b,
		cipher:  make([]byte, blockSize),
		out:     make([]byte, 0, bufSize),
		outUsed: 0,
	}

	copy(x.cipher, iv)
	return x
}

func (x *ofb) refill() {
	bs := x.b.BlockSize()
	remain := len(x.out) - x.outUsed
	if remain > x.outUsed {
		return
	}
	copy(x.out, x.out[x.outUsed:])
	x.out = x.out[:cap(x.out)]
	for remain < len(x.out)-bs {
		x.b.Encrypt(x.cipher, x.cipher)

			// larger batches for xor, and we should be
			// able to match CTR/OFB performance.
			copy(x.next[x.outUsed:], src)
		}
		n := subtle.XORBytes(dst, src, x.out[x.outUsed:])
		if !x.decrypt {
			copy(x.next[x.outUsed:], dst)
		}
		dst = dst[n:]
		src = src[n:]
		x.outUsed += n
	}
}

// NewCFBEncrypter returns a [Stream] which encrypts with cipher feedback mode,

		bufSize = block.BlockSize()
	}
	return &ctr{
		b:       block,
		ctr:     bytes.Clone(iv),
		out:     make([]byte, 0, bufSize),
		outUsed: 0,
	}
}

func (x *ctr) refill() {
	remain := len(x.out) - x.outUsed
	copy(x.out, x.out[x.outUsed:])
	x.out = x.out[:cap(x.out)]
	bs := x.b.BlockSize()
	for remain <= len(x.out)-bs {
		x.b.Encrypt(x.out[remain:], x.ctr)
		remain += bs

    TF_VariantTensor:$optional
  );
}

def TF_OutfeedEnqueueOp : TF_Op<"OutfeedEnqueue", []> {
  let summary = "Enqueue a Tensor on the computation outfeed.";

  let arguments = (ins
    Arg<TF_Tensor, [{A tensor that will be inserted into the outfeed queue.}]>:$input
  );

  let results = (outs);

  TF_DerivedOperandTypeAttr dtype = TF_DerivedOperandTypeAttr<0>;
}

	msghdr.Iovlen = int32(length)
}

func (cmsg *Cmsghdr) SetLen(length int) {
	cmsg.Len = uint32(length)
}

func sendfile(outfd int, infd int, offset *int64, count int) (written int, err error) {
	var writtenOut uint64 = 0
	_, _, e1 := Syscall9(SYS_SENDFILE, uintptr(infd), uintptr(outfd), uintptr(*offset), uintptr(count), 0, uintptr(unsafe.Pointer(&writtenOut)), 0, 0, 0)

	written = int(writtenOut)

	if e1 != 0 {
		err = e1

	msghdr.Controllen = uint32(length)
}

func (cmsg *Cmsghdr) SetLen(length int) {
	cmsg.Len = uint32(length)
}

func sendfile(outfd int, infd int, offset *int64, count int) (written int, err error) {
	var writtenOut uint64 = 0
	_, _, e1 := Syscall9(SYS_SENDFILE, uintptr(infd), uintptr(outfd), uintptr(*offset), uintptr((*offset)>>32), uintptr(count), 0, uintptr(unsafe.Pointer(&writtenOut)), 0, 0)

	written = int(writtenOut)

	cmsg.Len = uint32(length)
}

func (d *PtraceIoDesc) SetLen(length int) {
	d.Len = uint64(length)
}

func sendfile(outfd int, infd int, offset *int64, count int) (written int, err error) {
	var writtenOut uint64 = 0
	_, _, e1 := Syscall9(SYS_SENDFILE, uintptr(infd), uintptr(outfd), uintptr(*offset), uintptr(count), 0, uintptr(unsafe.Pointer(&writtenOut)), 0, 0, 0)

	written = int(writtenOut)

	if e1 != 0 {
		err = e1

	cmsg.Len = uint32(length)
}

func (d *PtraceIoDesc) SetLen(length int) {
	d.Len = uint32(length)
}

func sendfile(outfd int, infd int, offset *int64, count int) (written int, err error) {
	var writtenOut uint64 = 0
	_, _, e1 := Syscall9(SYS_SENDFILE, uintptr(infd), uintptr(outfd), uintptr(*offset), uintptr((*offset)>>32), uintptr(count), 0, uintptr(unsafe.Pointer(&writtenOut)), 0, 0)

	written = int(writtenOut)