// CHECK: %[[lstm:.*]] = "tfl.unidirectional_sequence_lstm"(%[[input_0]], %[[input_1]], %[[input_2]], %[[input_3]], %[[input_4]], %[[input_5]], %[[input_6]], %[[input_7]], %[[input_8]],
// CHECK-SAME: %[[input_9]], %[[input_10]], %[[input_11]], %[[input_12]], %[[input_13]], %[[input_14]], %[[input_15]], %[[input_16]], %[[input_17]], %[[input_18]], %[[input_19]],
// CHECK-SAME: %[[input_20]], %[[input_21]], %[[input_22]], %[[input_23]])

    p.device = &dummy_device;
    p.step_id = 43;

    Tensor t(tensorflow::uint8(123));

    gtl::InlinedVector<TensorValue, 4> inputs;
    // Simulate 2 inputs
    inputs.emplace_back(&t);
    inputs.emplace_back();
    p.inputs = inputs;

    Status status;
    std::unique_ptr<OpKernel> kernel =
        GetFakeKernel(device_name, op_name, node_name, &status);
    TF_EXPECT_OK(status);

  EXPECT_THAT(input2->type, Eq(TensorType_INT8));

  // Check if the quantization params of the minimum/maximum inputs match
  // after requantization
  EXPECT_THAT(input1->quantization->scale, Eq(input2->quantization->scale));
  EXPECT_THAT(input1->quantization->zero_point,
              Eq(input2->quantization->zero_point));

  // Check the input quantization params match the output ones.

sign.input 9d61b19deffd5a60ba84:d75a980182b10ab7d54b::e5564300c360ac729086: 4ccd089b28ff96da9db6:3d4017c3e843895a92b7:72:92a009a9f0d4cab8720e: c5aa8df43f9f837bedb7:fc51cd8e6218a1a38da4:af82:6291d657deec24024827: 0d4a05b07352a5436e18:e61a185bcef2613a6c7c:cbc77b:d9868d52c2bebce5f3fa: 6df9340c138cc188b5fe:c0dac102c4533186e25d:5f4c8989:124f6fc6b0d100842769: b780381a65edf8b78f69:e253af0766804b869bb1:18b6bec097:b2fc46ad47af464478c1: 78ae9effe6f245e924a7:fbcfbfa40505d7f2be44:89010d855972:6ed629fc1d9ce9e14687:...

  TFE_DeleteContextOptions(opts);

  TFE_TensorHandle* input1 = TestMatrixTensorHandle(ctx);
  TFE_TensorHandle* input2 = TestMatrixTensorHandle(ctx);
  TFE_Op* identityOp = TFE_NewOp(ctx, "IdentityN", status);
  CHECK_EQ(TF_OK, TF_GetCode(status)) << TF_Message(status);
  TFE_TensorHandle* inputs[] = {input1, input2};
  TFE_OpAddInputList(identityOp, inputs, 2, status);
  CHECK_EQ(TF_OK, TF_GetCode(status)) << TF_Message(status);

      (BinaryOp (TFL_ReshapeOp:$lhs $input1, (Arith_ConstantOp:$shape1 $s1)),
                (TFL_ReshapeOp:$rhs $input2, (Arith_ConstantOp:$shape2 $s2))),
      (TFL_ReshapeOp (BinaryOp $input1, $input2), $shape1),
      [(IsTailOfShape $rhs, $lhs),
       (IsTailOfShape $lhs, $rhs),
       (IsTailOfShape $input1, $input2),
       (IsTailOfShape $input2, $input1),
       (SameElementType $input1, $input2)]>;

overlapping-outputs-input-comparison.png...

  // The first one will the function's output
  std::vector<TF_Output> outputs;

  // Add while loop to func_graph_
  {
    // The inputs to the while loop
    std::vector<TF_Output> inputs = {{feed1, 0}, {feed2, 0}};
    std::unique_ptr<TF_WhileParams> params(new TF_WhileParams(
        TF_NewWhile(func_graph_, &inputs[0], inputs.size(), s_)));

    subject:
      - foo Object
    inputs:
      - other.thing String (java.lang.String) [*]

  by-type
    subject:
      - <no path> Runnable [*]
    inputs:
      - <no path> String (java.lang.String) [*]

  creator
    inputs:
      - a.b Object (a.b) [*]
'''
    }

    def "closes elements as required to bind all subjects and inputs"() {
        given:

It is possible to temporarily opt out of configuration input detection in the following cases:

* Since Gradle 8.1, using many APIs related to the file system is correctly tracked as configuration inputs, including the file system checks, such as `File.exists()` or `File.isFile()`.
+