replicated TPU computation. The number of times a TPU computation is
    replicated is defined in the `tf.TPUReplicateMetadata` op (`num_replicas`
    attribute) and operand and result sizes of `tf.TPUReplicatedInput` and
    `tf.TPUReplicatedOutput` respectively must match, excluding packed tensors.
    It is also assumed ops of the same TPU computation do not have ops outside

#include "tensorflow/core/protobuf/config.pb.h"
#include "tensorflow/core/util/command_line_flags.h"

namespace tensorflow {

struct XlaAutoJitFlag {
  // Control compilation of operators into XLA computations on CPU and GPU
  // devices.  0 = use ConfigProto setting; -1 = off; 1 = on for things very
  // likely to be improved; 2 = on for everything.
  //

void AppendMarkForCompilationPassFlagsInternal(std::vector<Flag>* flag_list) {
  std::vector<Flag> new_flags = {
      Flag("tf_xla_auto_jit", SetterForXlaAutoJitFlag, "0",
           "Control compilation of operators into XLA computations on CPU and "
           "GPU devices.  0 = use ConfigProto setting; -1 = off; 1 = on for "
           "things very likely to be improved; 2 = on for everything; "

	// 	x1:x0 := r.Uint64()
	// 	0:hi, lo1:lo0 := bits.Mul64(x1:x0, 0:n)
	// Writing out the multiplication in terms of bits.Mul32 allows
	// using direct hardware instructions and avoiding
	// the computations involving these zeros.
	x := r.Uint64()
	lo1a, lo0 := bits.Mul32(uint32(x), n)
	hi, lo1b := bits.Mul32(uint32(x>>32), n)
	lo1, c := bits.Add32(lo1a, lo1b, 0)
	hi += c
	if lo1 == 0 && lo0 < uint32(n) {

    // done().
    // The latter may be too conservative, but given the host is involved in
    // waiting for the transfer to complete anyway there is probably little
    // downside. If we were to add the ability for computations to wait directly
    // on transfers, then we might want to rethink this property.
    // Also ideally this host callback should be on source stream rather than

// between theoretical and numerical jacobians:
//   fabs(jac_t - jac_n) / max(fabs(jac_t), fabs(jac_n))

// TODO(andydavis) Vectorize and/or multi-thread Jacobian computations if
// performance becomes an issue.

// BaseUnitsForType provides a list of typed unit values for each basis in the
// requested type.
// When T is real,

	GOROOTpkg string
	GOROOTsrc string

	GOBIN                         = Getenv("GOBIN")
	GOMODCACHE, GOMODCACHEChanged = EnvOrAndChanged("GOMODCACHE", gopathDir("pkg/mod"))

	// Used in envcmd.MkEnv and build ID computations.
	GOARM64, goARM64Changed     = EnvOrAndChanged("GOARM64", fmt.Sprint(buildcfg.GOARM64))
	GOARM, goARMChanged         = EnvOrAndChanged("GOARM", fmt.Sprint(buildcfg.GOARM))

	atomic.StoreInt64(&connectionNumber, 0)
}

// initProxyMetadata initializes just the basic metadata of a proxy. This is decoupled from
// initProxyState such that we can perform authorization before attempting expensive computations to
// fully initialize the proxy.
func (s *DiscoveryServer) initProxyMetadata(node *core.Node) (*model.Proxy, error) {
	meta, err := model.ParseMetadata(node.Metadata)
	if err != nil {

        [](Operation* user) { return isa<TF::AssignVariableOp>(user); });

    // TODO(b/186717563): Eliminate all pass through results once XLA correctly
    // handles empty computations. Another approach could be to drop empty
    // clusters within MLIR but that seems to trigger other failures but can be
    // considered again.
    // Old bridge only removes unsupported TPU types (only string for now)

  // Internal tensors are those that do not flow in or out of the operation,
  // but instead are part of internal computation. As such, the operation's
  // implementation may manage its memory more efficiently. They are needed
  // however (i.e. not just an implementation detail) since they are part of the
  // computation, which may require relevant metadata such as quantization
  // parameters.
  intermediates:[int];