}
        }
    }

    @Test
    fun `same script different target type`() {

        // given: same init, settings & build files all applying same script
        val same = withFile("same.gradle.kts", """println("Same script on ${'$'}this")""")
        val sameApply = """apply(from = "same.gradle.kts")"""
        val initScriptFile = withFile("same.init.gradle.kts", sameApply)

// CHECK-LABEL: @batchNormInference_2D_inner_features
// CHECK-SAME: %[[X:[^:[:space:]]+]]
// CHECK-SAME: %[[SCALE:[^:[:space:]]+]]
// CHECK-SAME: %[[OFFSET:[^:[:space:]]+]]
// CHECK-SAME: %[[MEAN:[^:[:space:]]+]]
// CHECK-SAME: %[[VARIANCE:[^:[:space:]]+]]
func.func @batchNormInference_2D_inner_features(

// CHECK-SAME: proj_clip = 0.000000e+00 : f32,
// CHECK-SAME: time_major = false}> : (
// CHECK-SAME: tensor<1x2x3xf32>,
// CHECK-SAME: tensor<1x1xf32>, tensor<1x1xf32>, tensor<1x1xf32>, tensor<1x1xf32>,
// CHECK-SAME: tensor<1x1xf32>, tensor<1x1xf32>, tensor<1x1xf32>, tensor<1x1xf32>,
// CHECK-SAME: none, none, none,
// CHECK-SAME: tensor<3xf32>, tensor<3xf32>, tensor<3xf32>, tensor<3xf32>,

// CHECK-SAME: , asymmetric_quantize_inputs = true
// CHECK: return %[[mm:.*]]

// PerTensor: %[[w:.*]] = "tfl.pseudo_qconst"() <{qtype = tensor<512x12x!quant.uniform<i8<-127:127>:f32, 1.000000e+00>>,
// PerTensor: %[[mm:.*]] = "tfl.batch_matmul"(%arg0, %[[w]]) <{adj_x = false, adj_y = false
// PerTensor-SAME: , asymmetric_quantize_inputs = true
// PerTensor: return %[[mm:.*]]
}

  // FB-SAME:  -8.0, -9.0, -10.0, -11.0, -12.0, -13.0, -14.0, -15.0, -16.0,
  // FB-SAME:  -17.0, -18.0, -19.0, -20.0, -21.0, -22.0, -23.0, -24.0, -25.0,
  // FB-SAME:  -26.0, -27.0, -28.0, -29.0, -30.0, -31.0, -32.0, -33.0, -34.0,
  // FB-SAME:  -35.0, -36.0, -37.0, -38.0, -39.0 ],
  // FB-NEXT:        max: [ 0.0, 1.0, 2.0, 3.0, 4.0, 5.0, 6.0, 7.0, 8.0, 9.0,

  func.return %3 : tensor<4x8xf32>
}

// CHECK-LABEL:   func @select(
// CHECK-SAME:                 %[[VAL_0:.*]]: tensor<2xi1>,
// CHECK-SAME:                 %[[VAL_1:.*]]: tensor<2xi32>,
// CHECK-SAME:                 %[[VAL_2:.*]]: tensor<2xi32>) -> tensor<2xi32> {

// RUN: tf-opt -split-input-file -verify-diagnostics -xla-legalize-tf-collective -xla-legalize-tf %s | FileCheck %s


// CHECK: module attributes
// CHECK-SAME{LITERAL}: tf2xla.collective_info.group_key = 0
// CHECK-SAME{LITERAL}: tf2xla.collective_info.group_size = 2
// CHECK-LABEL: func @all_reduce_cross_replica
func.func @all_reduce_cross_replica(%input: tensor<f32>) -> tensor<f32> {

  func.return
}

// CHECK:      tf_device.replicate
// CHECK-SAME: [%[[ARG_0]], %[[ARG_0]]] as %{{[a-z0-9]*}}
// CHECK-SAME: [%[[ARG_2]], %[[ARG_2]]] as %{{[a-z0-9]*}}
// CHECK-SAME: [%[[ARG_4]], %[[ARG_4]]] as %{{[a-z0-9]*}}
// CHECK-SAME: %[[ARG_1]] as %{{[a-z0-9]*}}
// CHECK-SAME: %[[ARG_3]] as %{{[a-z0-9]*}}
// CHECK-SAME: %[[ARG_5]] as %{{[a-z0-9]*}}

	netns1 := p.UpsertPodCacheWithNetns(string(pod.UID), wl)
	if !reflect.DeepEqual(netns1, ns) {
		t.Fatalf("Expected the same Netns for the same uid, got %v and %v", netns1, ns)
	}

	ns2 := newFakeNsInode(inc(), 1)
	wl2 := WorkloadInfo{
		Workload: podToWorkload(pod),
		Netns:    ns2,
	}
	// when using same uid, the original netns should be returned
	netns2 := p.UpsertPodCacheWithNetns(string(pod.UID), wl2)
	if netns2 != ns {

// CHECK: return %[[CALL]]

// CHECK: quantized_dot_general_fn
// CHECK-SAME: (%[[ARG1:.+]]: tensor<1x2xf32>,  %[[ARG2:.+]]: tensor<2x3x!quant.uniform<i8<-127:127>:f32, 0.0023622048182750312>>) -> tensor<1x3xf32>
// CHECK: %[[DOT:.+]] = stablehlo.dot_general %[[ARG1]], %[[ARG2]]
// CHECK-SAME: (tensor<1x2xf32>, tensor<2x3x!quant.uniform<i8<-127:127>:f32, 0.0023622048182750312>>) -> tensor<1x3xf32>