SYS_THR_CREATE               = 430 // { int thr_create(ucontext_t *ctx, long *id, \
	SYS_THR_EXIT                 = 431 // { void thr_exit(long *state); }
	SYS_THR_SELF                 = 432 // { int thr_self(long *id); }
	SYS_THR_KILL                 = 433 // { int thr_kill(long id, int sig); }
	SYS__UMTX_LOCK               = 434 // { int _umtx_lock(struct umtx *umtx); }

//
//   return values.
//
//
//
// Computed values:
//
//   eval z = z[0] + (z[1] << 64) + (z[2] << 128) + (z[3] << 192)
//

	var b [8]byte
	for i := range b {
		b[i] = r.data.b[pos%uint64(len(r.data.b))]
		pos++
	}
	return uint64(b[0]) | uint64(b[1])<<8 |
		uint64(b[2])<<16 | uint64(b[3])<<24 |
		uint64(b[4])<<32 | uint64(b[5])<<40 |
		uint64(b[6])<<48 | uint64(b[7])<<56
}

func (r *debugLogReader) peek() (tick uint64) {
	// Consume any sync records.
	size := uint64(0)
	for size == 0 {

  //
  // The data_buffer itself is an opaque container, with the assumption that the
  // target device is little-endian. In addition, all builtin operators assume
  // the memory is ordered such that if `shape` is [4, 3, 2], then index
  // [i, j, k] maps to data_buffer[i*3*2 + j*2 + k].
  buffer:uint;
  name:string;  // For debugging and importing back into tensorflow.
  quantization:QuantizationParameters;  // Optional.

	{"(DX)", "(DX)"},
	{"(R9)", "(R9)"},
	{"(R9)(BX*8)", "(R9)(BX*8)"},
	{"(SI)", "(SI)"},
	{"(SI)(BX*1)", "(SI)(BX*1)"},
	{"(SI)(DX*1)", "(SI)(DX*1)"},
	{"(SP)", "(SP)"},
	{"(SP)(AX*4)", "(SP)(AX*4)"},
	{"32(SP)(BX*2)", "32(SP)(BX*2)"},
	{"32323(SP)(R8*4)", "32323(SP)(R8*4)"},
	{"+3(PC)", "3(PC)"},
	{"-1(DI)(BX*1)", "-1(DI)(BX*1)"},
	{"-3(PC)", "-3(PC)"},
	{"-64(SI)(BX*1)", "-64(SI)(BX*1)"},

//
//   return values.
//
//
//
// Computed values:
//
//   eval z = z[0] + (z[1] << 64) + (z[2] << 128) + (z[3] << 192)
//

//
//   return values.
//
//
//
// Computed values:
//
//   eval z = z[0] + (z[1] << 64) + (z[2] << 128) + (z[3] << 192)
//

	klGLIndex         ID = 426
	klnIndex          ID = 427
	klnKEIndex        ID = 428
	kmIndex           ID = 429
	kmKHIndex         ID = 430
	knIndex           ID = 431
	knINIndex         ID = 432
	koIndex           ID = 433
	koKPIndex         ID = 434
	koKRIndex         ID = 435
	kokIndex          ID = 436
	kokINIndex        ID = 437
	ksIndex           ID = 438
	ksINIndex         ID = 439

  //
  // The data_buffer itself is an opaque container, with the assumption that the
  // target device is little-endian. In addition, all builtin operators assume
  // the memory is ordered such that if `shape` is [4, 3, 2], then index
  // [i, j, k] maps to data_buffer[i*3*2 + j*2 + k].
  buffer:uint;
  name:string;  // For debugging and importing back into tensorflow.
  quantization:QuantizationParameters;  // Optional.

	SYS_THR_CREATE               = 430 // { int thr_create(ucontext_t *ctx, long *id, int flags); }
	SYS_THR_EXIT                 = 431 // { void thr_exit(long *state); }
	SYS_THR_SELF                 = 432 // { int thr_self(long *id); }
	SYS_THR_KILL                 = 433 // { int thr_kill(long id, int sig); }
	SYS_JAIL_ATTACH              = 436 // { int jail_attach(int jid); }