hasher.putString(element.toString())
            }
        }
        return hasher.hash()
    }

    private
    suspend fun SequenceScope<Failure>.visitFailures(failure: Failure) {
        yield(failure)
        failure.suppressed.forEach { visitFailures(it) }
        failure.causes.forEach { visitFailures(it) }
    }

    from docs_src.sql_databases.sql_app_py39 import main

    # Ensure import side effects are re-executed
    importlib.reload(main)
    with TestClient(main.app) as c:
        yield c
    if test_db.is_file():  # pragma: nocover
        test_db.unlink()
    os.chdir(cwd)


@needs_py39
# TODO: pv2 add version with Pydantic v2
@needs_pydanticv1
def test_create_user(client):

                doLast {

                    // Coroutines are no longer experimental
                    val coroutine = sequence {
                        // Unsigned integer types
                        yield(42UL)
                    }

                    // Capturing when
                    when (val value = coroutine.first()) {
                        42UL -> print("42!")

  // Inline the region in place of the IfRegion op, and forward the yield
  // inputs to the IfRegion op results. This is possible only if the yield
  // types match the result types.
  auto yield = cast<YieldOp>(region.front().getTerminator());
  auto updated_results = llvm::to_vector<4>(yield.getOperands());

  // If the yield types do not match the IfRegion result types, add appropriate
  // casts.

func.func private @cond(%arg0: !tf_res, %arg1: !tf_res) -> (tensor<i32>) {
  %graph = tf_executor.graph {
    %island, %ctrl = tf_executor.island {
      %pred = "tf.SomeOp"() : () -> tensor<i32>
      tf_executor.yield %pred : tensor<i32>
    }
    tf_executor.fetch %island : tensor<i32>
  }
  func.return %graph : tensor<i32>
}

// CHECK-LABEL: @test_while_loop

  regions accept the current value of the iteration variables as inputs.

  The condition region yields a tensor<i1> which, if false, will exit the loop.
  It can also, optionally and additionally, yield the iteration variables, which
  must be unchanged.

  The body region always has to yield the (possibly updated) iteration variables.

  The iteration variables are initialized to the Op input, and the results of the

      }
    }
  }
  return invariant;
}

Operation* GetAnyReturn(Region* region) {
  Operation* result;
  // We only go one level deep, since "returns" in nested functions
  // and return-as-yield CF don't belong to us.
  for (Block& block : region->getBlocks()) {
    for (Operation& op : block.getOperations()) {
      if (op.hasTrait<OpTrait::ReturnLike>()) result = &op;
    }
  }
  return result;
}

  }
  void runOnOperation() override;
};

// Creates a call to function `func` in region `caller_region`. Use `args` as
// the call arguments, and terminate the region with a yield. The arguments are
// cast to the required type before the call. `use_region_args` control whether
// the input arguments are used as is (for IfOp) or block arguments of the same

        newStartedThread(
            new CheckedRunnable() {
              @Override
              public void realRun() {
                while (!a.compareAndSet(0, 2.0, 3.0)) {
                  Thread.yield();
                }
              }
            });

    assertTrue(a.compareAndSet(0, 1.0, 2.0));
    awaitTermination(t);
    assertBitEquals(3.0, a.get(0));
  }

      %6 = "tf.Identity"(%output_6) {device = ""} : (tensor<1x3xf32>) -> tensor<1x3xf32>
      "tf.Yield"(%4, %6) {device = ""} : (tensor<i1>, tensor<1x3xf32>) -> ()
    }, {
      %4 = "tf.Identity"(%cst_3) {device = ""} : (tensor<i1>) -> tensor<i1>