// CHECK-NEXT: %[[q:.*]] = "tfl.quantize"(%arg0) <{qtype = tensor<1x224x224x3x!quant.uniform<i8:f32, 7.812500e-03>>}> : (tensor<1x224x224x3xf32>) -> tensor<1x224x224x3x!quant.uniform<i8:f32, 7.812500e-03>>
// CHECK-NEXT: %[[cst1:.*]] = "tfl.pseudo_qconst"() <{qtype = tensor<32x3x3x3x!quant.uniform<i8<-127:127>:f32, 0.021826678373682216:151>>, value = dense<-76> : tensor<32x3x3x3xi8>}> : () -> tensor<32x3x3x3x!quant.uniform<i8<-127:127>:f32, 0.021826678373682216>>

    // CHECK: %[[CST0:.*]] = stablehlo.constant dense<0xFF800000> : tensor<f32>
    // CHECK: %[[CST1:.*]] = stablehlo.constant dense<0xFF80000E> : tensor<2x3x1024x3xf32>
    // CHECK: %[[Q0:.*]] = "quantfork.qcast"(%[[CST0]])
    // CHECK: %[[Q1:.*]] = "quantfork.qcast"(%[[CST1]])
    // CHECK: %[[Q2:.*]] = "quantfork.qcast"(%[[ARG0]])
    // CHECK: %[[CALL:.*]] = call @quantized_dot_general_fn_1(%[[Q2]], %[[Q1]])

  %cst0 = arith.constant dense<[[1.0, 2.0], [3.0, 4.0]]> : tensor<2x2xf32>
  %cst1 = arith.constant dense<2.0> : tensor<2xf32>
  %cst2 = arith.constant dense<[1.0, 2.0]> : tensor<2xf32>

  %cst1 = "tfl.pseudo_const" () {value = dense<0.0> : tensor<1x4xf32>} : () -> tensor<1x4xf32> loc("Const")

    attributes {tf.entry_function = {outputs = "result"}} {
  %cst = arith.constant dense<1> : tensor<i32> loc("dec")
  %cst0 = arith.constant dense<5> : tensor<i32> loc("N")
  %cst1 = arith.constant dense<3.0> : tensor<1xf32> loc("val")
  %0:2 = "tfl.while"(%cst0, %cst1) ({
    ^bb0(%arg2: tensor<*xi32>, %arg3: tensor<*xf32>):
      // CHECK: call @WhileOp_cond
      // CHECK-SAME: (tensor<*xi32>, tensor<*xf32>)

  // CHECK-DAG: %[[CST1:.*]] = arith.constant dense<1> : tensor<4xi32>
  // CHECK-DAG: %[[cst_1:.*]] = arith.constant dense<[1, 2, 3, 1]> : tensor<4xi32>
  // CHECK: %0 = "tf.Reshape"(%arg0, %[[cst_1]]) : (tensor<2x3xf32>, tensor<4xi32>) -> tensor<1x2x3x1xf32>

// CHECK: %[[cst1:.*]] = "tfl.pseudo_qconst"() <{qtype = tensor<32x3x3x3x!quant.uniform<u8<1:255>:f32, 1.000000e-01>>, value = dense<1> : tensor<32x3x3x3xi8>}>
// CHECK: %[[conv:.*]] = "tfl.conv_2d"(%arg0, %[[cst1]], %[[cst0]])
// CHECK: return %[[conv]] : tensor<1x112x112x32x!quant.uniform<u8:f32, 0.023528476789885875>>
}

		o.Write(k)

		kk, err := Decapsulate(dk, ct)
		if err != nil {
			t.Fatal(err)
		}
		if !bytes.Equal(kk, k) {
			t.Errorf("k: got %x, expected %x", kk, k)
		}

		s.Read(ct1)
		k1, err := Decapsulate(dk, ct1)
		if err != nil {
			t.Fatal(err)
		}
		o.Write(k1)
	}

	got := hex.EncodeToString(o.Sum(nil))
	if got != expected {
		t.Errorf("got %s, expected %s", got, expected)
	}
}

                }

            repositories {
                ivy {
                    // Url of Android Studio archive
                    url = uri("https://redirector.gvt1.com/edgedl/android/studio/ide-zips")
                    patternLayout {
                        artifact("[revision]/[artifact]-[revision]-[ext]")
                    }
                    metadataSources { artifact() }

        then:
        createdTasksFor(":a:t1") == [":a:t1"]
        createdTasksFor(":a:t1", "b:t1") == [":a:t1", ":b:t1"]
        createdTasksFor(":t1") == [":t1"]
        createdTasksFor("t1") == [":a:t1", ":b:t1", ":c:t1", ":t1"]
    }

    def "rule based tasks that are not requested on the command line are not created"() {
        when:
        buildFile << """
            ${ruleBasedTasks()}
            model {