return func(yield func(int, E) bool) {
		for i, v := range s {
			if !yield(i, v) {
				return
			}
		}
	}
}

// Backward returns an iterator over index-value pairs in the slice,
// traversing it backward. The indexes range from len(s)-1 down to 0.
func Backward[Slice ~[]E, E any](s Slice) iter.Seq2[int, E] {
	return func(yield func(int, E) bool) {
		for i := len(s) - 1; i >= 0; i-- {

    dep_a = generate_dep_a()
    try:
        yield dep_a
    finally:
        dep_a.close()


async def dependency_b(dep_a=Depends(dependency_a)):
    dep_b = generate_dep_b()
    try:
        yield dep_b
    finally:
        dep_b.close(dep_a)


async def dependency_c(dep_b=Depends(dependency_b)):
    dep_c = generate_dep_c()
    try:
        yield dep_c
    finally:

    dep_a = generate_dep_a()
    try:
        yield dep_a
    finally:
        dep_a.close()


async def dependency_b(dep_a=Depends(dependency_a)):
    dep_b = generate_dep_b()
    try:
        yield dep_b
    finally:
        dep_b.close(dep_a)


async def dependency_c(dep_b=Depends(dependency_b)):
    dep_c = generate_dep_c()
    try:
        yield dep_c
    finally:

Using resources from dependencies with `yield` in background tasks is no longer supported.

This change is what supports the new features, read below. 🤓

### Dependencies with `yield`, `HTTPException` and Background Tasks

Dependencies with `yield` now can raise `HTTPException` and other exceptions after `yield`. 🎉

from typing_extensions import Annotated


async def dependency_a():
    dep_a = generate_dep_a()
    try:
        yield dep_a
    finally:
        dep_a.close()


async def dependency_b(dep_a: Annotated[DepA, Depends(dependency_a)]):
    dep_b = generate_dep_b()
    try:
        yield dep_b
    finally:
        dep_b.close(dep_a)


async def dependency_c(dep_b: Annotated[DepB, Depends(dependency_b)]):

  ^bb0(%arg2: tensor<i32>, %arg3: tensor<i32>):
    %1 = "tf.OpA"() {is_stateless = true} : () -> tensor<i1>
    "tf.Yield"(%1) : (tensor<i1>) -> ()
  }, {
  ^bb0(%arg2: tensor<i32>, %arg3: tensor<i32>):
    %1 = "tf.GlobalIterId"() : () -> tensor<i64>
    "tf.Yield"(%arg2, %arg3) : (tensor<i32>, tensor<i32>) -> ()

      // CHECK-SAME: device = "/device:test_device:0"
      %3 = "tf_device.launch_func"(%2) {device = "/device:test_device:0", func = @_func} : (tensor<?xf32>) -> tensor<?xf32>

      // CHECK: tf_executor.yield %[[CALL_OUTPUT]]
      tf_executor.yield %3 : tensor<?xf32>
    }
    tf_executor.fetch %1#0 : tensor<?xf32>
  }
  func.return %0 : tensor<?xf32>
}

func.func @_func(%arg0: tensor<?xf32>) -> tensor<?xf32> {

    // CHECK: tf_executor.island wraps "tf.IdentityN"
    %0:3 = tf_executor.island {
      %1:2 = "tf.IdentityN"(%arg0, %arg1) : (tensor<i32>, tensor<f32>) -> (tensor<i32>, tensor<f32>) loc("identity@some_function")
      tf_executor.yield %1#0, %1#1 : tensor<i32>, tensor<f32> loc("identity@some_function")
    } loc("identity@some_function")
    tf_executor.fetch
  }
  func.return
}

// CHECK-LABEL: func @island_no_wrap_print_mismatched_results

// tf_executor.island, tf.Identity, and tf_executor.yield).

// CHECK-LABEL: "func.func"
// CHECK: sym_name = "island_one_op_all_locs_same"
// CHECK:    "tf_executor.graph"() ({
// CHECK-NEXT:      "tf_executor.island"() ({
// CHECK-NEXT:        "tf.Identity"(%{{.*}}) : (tensor<f32>) -> tensor<f32> loc("identity@some_function")
// CHECK-NEXT:        "tf_executor.yield"(%{{.*}}) : (tensor<f32>) -> () loc("identity@some_function")

        %5 = "tf.D"(%4) {device = "gpu0"} : (tensor<?xi32>) -> tensor<?xi32>
        // CHECK: tf_device.return %[[D_OUTPUT]]

        // CHECK: tf_executor.yield %[[GPU0_OUTPUT]]
        tf_executor.yield %5 : tensor<?xi32>
      }
      tf_executor.fetch %1#0 : tensor<?xi32>
    }
    func.return %0 : tensor<?xi32>
  }
}

// -----