// to the passed operator
func checkPolicyCond(op string, input1, input2 string) bool {
	switch op {
	case policyCondEqual:
		return input1 == input2
	case policyCondStartsWith:
		return strings.HasPrefix(input1, input2)
	}
	return false
}

// checkPostPolicy - apply policy conditions and validate input values.
// (http://docs.aws.amazon.com/AmazonS3/latest/API/sigv4-HTTPPOSTConstructPolicy.html)

      %outputs_5, %control_6 = tf_executor.island wraps "tf.TPUReplicatedInput"(%outputs_3) {N = 1 : i64, T = "tfdtype$DT_FLOAT", device = "", name = "input0"} : (tensor<0xf32>) -> tensor<0xf32>

        where:
        pathSensitivity           | normalizedPaths
        PathSensitivity.RELATIVE  | [modified: "in/some/subdir/input1.txt", added: "in/some/other/subdir/other-input.txt", removed: "in/some/subdir/input2.txt"]
        PathSensitivity.NAME_ONLY | [modified: "input1.txt", added: "other-input.txt", removed: "input2.txt"]
    }

    def "provides the file type"() {

    Node* input =
        ops::SourceOp("FakeNullary", builder.opts().WithName("Input"));
    Node* clustered_producer =
        ops::BinaryOp("FakeBinary", input, input,
                      builder.opts().WithName("ClusteredProducer"));
    // The first input is hostmem and the second input is devicemem.
    Node* consumer_in_different_cluster =
        ops::BinaryOp("FakeBinary", input, clustered_producer,

  // CHECK-SAME: %[[INPUT1:.*]]: tensor<1024x3xf32>, %[[INPUT2:.*]]: tensor<1024x3xf32>
  // CHECK: %[[CONSTANT2:.*]] = stablehlo.constant dense<1.000000e+03> : tensor<1024x3xf32>
  // CHECK: %[[ADD:.*]] = stablehlo.add %[[INPUT1]], %[[CONSTANT2]] : tensor<1024x3xf32>
  // CHECK: %[[MUL:.*]] = stablehlo.multiply %[[INPUT1]], %[[INPUT2]] : tensor<1024x3xf32>
  // CHECK: return %[[ADD]], %[[MUL]]

}

// CHECK-LABEL: func @tensor_array_while_test
// CHECK-SAME: ([[in_chain:%.*]]: !tfrt.chain
func.func @tensor_array_while_test(%indices: tensor<?xi32>, %input_0: tensor<?x?x?xf32>, %input_1: tensor<?x?x?xf32>) -> (tensor<?x?x512xf32>, tensor<?x?x512xf32>) {
  %index = "tf.Const"() {device = "/device:CPU:0", value = dense<0> : tensor<i32>} : () -> (tensor<i32>)

# RUN: tf-mlir-translate -graphdef-to-mlir -tf-enable-shape-inference-on-import=false %s -tf-input-arrays=input -tf-input-shapes='' -tf-output-arrays=input -tf-convert-legacy-fed-inputs -o - | FileCheck --check-prefix=NODATATYPE %s

        when:
        def input = ask { it.selectOption(TEXT, [11, 12, 13], 12) }

        then:
        1 * userInputReader.readInput() >> new UserInputReader.TextResponse("")
        0 * userInputHandler._

        and:
        input == 12
    }

    def "select question returns default on end-of-input"() {
        when:
        def input = ask { it.selectOption(TEXT, [11, 12, 13], 12) }

  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.

  params.op_kernel = kernel.get();
  AllocatorAttributes alloc_attrs;
  params.output_attr_array = &alloc_attrs;
  gtl::InlinedVector<TensorValue, 4> inputs;
  inputs.emplace_back(tags);
  inputs.emplace_back(values);
  params.inputs = inputs;
  OpKernelContext ctx(&params, 1);
  kernel->Compute(&ctx);
  ASSERT_EQ(expected_code, ctx.status().code());
  if (expected_code == error::OK) {
    Summary summary;