// CHECK-NEXT:   %[[MY_CUSTOM:.*]] = "tf.MyCustomOp"(%arg1, %arg2) {fused_activation_function = "RELU", int_attr = 2 : i32} : (tensor<4xf32>, tensor<4xf32>) -> tensor<4xf32>
// CHECK-NEXT:   "tfl.yield"(%[[MY_CUSTOM]]) : (tensor<4xf32>) -> ()
// CHECK-NEXT: }) {fused_activation_function = "RELU", int_attr = 2 : i32} : (tensor<4xf32>, tensor<4xf32>) -> tensor<4xf32>
// CHECK-NEXT: %[[CUSTOM_2:.*]] = "tfl.custom_tf"(%[[MUL]], %[[CST]]) ({

    pass


async def asyncgen_state(state: Dict[str, str] = Depends(get_state)):
    state["/async"] = "asyncgen started"
    yield state["/async"]
    state["/async"] = "asyncgen completed"


def generator_state(state: Dict[str, str] = Depends(get_state)):
    state["/sync"] = "generator started"
    yield state["/sync"]
    state["/sync"] = "generator completed"

			}
			ev++
			cnt++
		}
		if cnt != size {
			t.Errorf("read %d values expected %d", cnt, size)
		}
	}
}

func testSeq(yield func(int) bool) {
	for i := 0; i < 10; i += 2 {
		if !yield(i) {
			return
		}
	}
}

var testSeqResult = []int{0, 2, 4, 6, 8}

func TestAppendSeq(t *testing.T) {
	s := AppendSeq([]int{1, 2}, testSeq)

    var startIndex = 0
    while (true) {
        val endIndex = indexOf(delimiter, startIndex) + 1
        if (endIndex == 0) break
        yield(substring(startIndex, endIndex))
        startIndex = endIndex
    }
    if (startIndex <= lastIndex) {
        yield(substring(startIndex))
    }

			t.Errorf("Values(%v) = %v, want %v", m, got, want)
		}
	}
}

func TestInsert(t *testing.T) {
	got := map[int]int{
		1: 1,
		2: 1,
	}
	Insert(got, func(yield func(int, int) bool) {
		for i := 0; i < 10; i += 2 {
			if !yield(i, i+1) {
				return
			}
		}
	})

	want := map[int]int{
		1: 1,
		2: 1,
	}
	for i, v := range map[int]int{
		0: 1,
		2: 3,
		4: 5,
		6: 7,
		8: 9,

      %reduce_next =  tfr.call @tf__risc_add_dummy(%reduce_iter, %v) : (!tfr.tensor, !tfr.tensor) -> !tfr.tensor
      scf.yield %reduce_next : !tfr.tensor
    }
    scf.yield %reduce : !tfr.tensor
  }
  tfr.return %res : !tfr.tensor
}

tfr.func @tf__my_add_n_(!tfr.tensor_list<N,T>, i64 {tfr.name="N"}) -> !tfr.tensor attributes{N,T}

     & ⤴️ 👥 🔐 ⚫️ `finally` 🍫.

    👉 🌌 👥 ⚒ 💭 💽 🎉 🕧 📪 ⏮️ 📨. 🚥 📤 ⚠ ⏪ 🏭 📨.

    ✋️ 👆 💪 🚫 🤚 ➕1️⃣ ⚠ ⚪️➡️ 🚪 📟 (⏮️ `yield`). 👀 🌖 [🔗 ⏮️ `yield` & `HTTPException`](dependencies/dependencies-with-yield.md#yield-httpexception){.internal-link target=_blank}

& ⤴️, 🕐❔ ⚙️ 🔗 *➡ 🛠️ 🔢*, 👥 📣 ⚫️ ⏮️ 🆎 `Session` 👥 🗄 🔗 ⚪️➡️ 🇸🇲.

      %reduce_next =  tfr.call @tf__risc_concat(%reduce_iter, %temp1, %axis) : (!tfr.tensor, !tfr.tensor, i32) -> !tfr.tensor
      scf.yield %reduce_next : !tfr.tensor
    }
    scf.yield %reduce : !tfr.tensor
  }
  tfr.return %res : !tfr.tensor
}

tfr.func @tf__my_add_n(%values: !tfr.tensor_list,
                       %n: i32 {tfr.name="N"}) -> !tfr.tensor {

                    val ktSymbol = descriptor.takeIf { nameFilter(it.name) }?.toKtCallableSymbol(analysisContext) ?: continue
                    yield(ktSymbol)
                }
            }
        }
    }

    override fun getCallableSymbols(names: Collection<Name>): Sequence<KaCallableSymbol> = withValidityAssertion {

        rng.uniform(low=0.0, high=1.0, size=static_input_shape).astype(
            np.float32
        )
    )

    def data_gen() -> repr_dataset.RepresentativeDataset:
      for _ in range(100):
        yield {
            'input_tensor': rng.uniform(
                low=0.0, high=1.0, size=static_input_shape
            ).astype(np.float32)
        }

    dataset_path = self.create_tempfile('tfrecord').full_path