}
	return r
}

// Users adds the specified users as the subjects of the RoleBinding.
func (r *RoleBindingBuilder) Users(users ...string) *RoleBindingBuilder {
	for _, user := range users {
		r.RoleBinding.Subjects = append(r.RoleBinding.Subjects, rbacv1.Subject{Kind: rbacv1.UserKind, APIGroup: GroupName, Name: user})
	}
	return r
}

**Please note that when AD/LDAP is configured, MinIO will not support long term users defined internally.** Only AD/LDAP users (and the root user) are allowed. In addition to this, the server will not support operations on users or groups using `mc admin user` or `mc admin group` commands except `mc admin user info` and `mc admin group info` to list set policies for users and groups. This is because users and groups are defined externally in AD/LDAP.

    user = fake_decode_token(token)
    return user


@app.get("/users/me")
async def read_users_me(current_user: Annotated[User, Depends(get_current_user)]):

	// __TEXT                      1029a0000-1033bc000    [ 10.1M  7360K     0K     0K] r-x/rwx SM=COW          /Users/USER/*/gopls
	// __DATA_CONST                1033bc000-1035bc000    [ 2048K  2000K     0K     0K] r--/rwSM=COW          /Users/USER/*/gopls
	// __DATA_CONST                1035bc000-103a48000    [ 4656K  3824K     0K     0K] r--/rwSM=COW          /Users/USER/*/gopls

                                                           Operation *user) {
          if (!isa<IdentityOp>(user)) {
            variable_v2_op.emitOpError()
                << "expects all users to be 'tf.Identity', but got user "
                << user->getName();
            return false;
          }
          return true;
        })) {

	users := set.NewStringSet()
	for _, kv := range kvs {
		user := extractPathPrefixAndSuffix(string(kv.Key), prefix, path.Base(string(kv.Key)))
		users.Add(user)
	}
	return users
}

// Extract path string by stripping off the `prefix` value and the suffix,
// value, usually in the following form.
//
//	s := "config/iam/users/foo/config.json"
//	prefix := "config/iam/users/"

    SmallVector<Operation*> users_to_erase;
    for (auto user : users) {
      llvm::dyn_cast<mlir::stablehlo::UniformDequantizeOp>(user)
          .replaceAllUsesWith(new_call_op.getResult(0));
      users_to_erase.push_back(user);
    }
    for (auto user : users_to_erase) rewriter.eraseOp(user);
    rewriter.eraseOp(call_op);
    func_op.eraseResult(0);

package third_party.tensorflow.compiler.mlir.lite.experimental.tac;

// A list of filters for TAC users to run ops/functions on ML hardwares. The
// intuition is that, for ops/functions that can be run on ML hardware (e.g.
// EdgeTPU) and TFLite CPU, TAC users give a hint that they're more performant
// to run on TFLite CPU. These filters give the TAC users freedom to specify the
// parts that they want to use other hardware to accelerate.
message TacFilters {

contexts:
- context:
    cluster: cluster-a
    namespace: ns-a
    user: user-a
  name: context-a
- context:
    cluster: cluster-b
    namespace: ns-b
    user: user-b
  name: context-b
current-context: context-b
users:
- name: user-a
  user:
    client-certificate: mycertvalid.crt
    client-key: mycertvalid.key
- name: user-b
  user:
    client-certificate: mycertvalid.crt
    client-key: mycertvalid.key

app = FastAPI()

security = HTTPDigest()


@app.get("/users/me")
def read_current_user(credentials: HTTPAuthorizationCredentials = Security(security)):
    return {"scheme": credentials.scheme, "credentials": credentials.credentials}


client = TestClient(app)


def test_security_http_digest():
    response = client.get("/users/me", headers={"Authorization": "Digest foobar"})