_, err = GetLoadBalancerSourceRanges(&svc)
		if err == nil {
			t.Errorf("Expected error parsing: %q", v)
		}
	}
	checkError("10.0.0.1/33")
	checkError("foo.bar")
	checkError("10.0.0.1/32,*")
	checkError("10.0.0.1/32,")
	checkError("10.0.0.1/32, ")
	checkError("10.0.0.1")

	checkOK := func(v string) utilnet.IPNetSet {
		t.Helper()
		annotations := make(map[string]string)

    "vhlo.return_v1"(%lhs) : (tensor<f32>) -> ()
  }): (tensor<200x100x300xf32>, tensor<10x2xi32>, tensor<10x300xf32>) ->
      tensor<200x100x300xf32>
  func.return %0 : tensor<200x100x300xf32>
}

// CHECK-LABEL: func.func private @scatter(%arg0: tensor<200x100x300xf32>, %arg1: tensor<10x2xi32>, %arg2: tensor<10x300xf32>) -> tensor<200x100x300xf32> {

"args_5"}, %arg6: tensor<?x!tf_type.string> {tf._user_specified_name = "args_6"}, %arg7: tensor<?x?x512xf32> {tf._user_specified_name = "args_7"}, %arg8: tensor<?x?xf32> {tf._user_specified_name = "args_8"}, %arg9: tensor<?x300xi32> {tf._user_specified_name = "args_9"}, %arg10: tensor<?x300xi32> {tf._user_specified_name = "args_10"}, %arg11: tensor<?x300xf32> {tf._user_specified_name = "args_11"}, %arg12: tensor<?x300xf32> {tf._user_specified_name = "args_12"}, %arg13: tensor<?x!tf_type.string> {tf._user_specified_name...

// CHECK-DAG: %[[SCALE:.*]] = "tf.Const"() <{value = dense<0.0787401571> : tensor<f32>
}

// -----

module {
  func.func @quantize_xlagather(%arg0: tensor<10x2xi32>) -> tensor<1x300x10xf32> {
    %cst_0 = "tf.Const"() {device = "", value = dense<1.000000e+01> : tensor<200x100x300xf32>} : () -> tensor<200x100x300xf32>

		}
	}
}

func TestIPBitLen(t *testing.T) {
	tests := []struct {
		ip   Addr
		want int
	}{
		{Addr{}, 0},
		{mustIP("0.0.0.0"), 32},
		{mustIP("10.0.0.1"), 32},
		{mustIP("::"), 128},
		{mustIP("fed0::1"), 128},
		{mustIP("::ffff:10.0.0.1"), 128},
	}
	for _, tt := range tests {
		got := tt.ip.BitLen()
		if got != tt.want {

// CHECK-LABEL: func @func_with_call
func.func private @some_func(%arg0: tensor<100xf32>) -> tensor<100xf32> attributes {tf.api_implements = "lstm_b4e9f0e7-ac55-42bc-8ef2-8496419a608c"}
func.func @func_with_call(%arg0: tensor<100xf32>) -> tensor<100xf32> {
  %0 = func.call @some_func(%arg0) : (tensor<100xf32>) -> tensor<100xf32>
  func.return %0 : tensor<100xf32>
  }
}

// -----

module {

  %const_64_1 = "tf.Const"() {value = dense<[64, 1]> : tensor<2xi32>} : () -> tensor<2xi32>
  %1 = "tf.Range"(%const_0, %const_10000, %const_1) : (tensor<i32>, tensor<i32>, tensor<i32>) -> tensor<10000xi32>
  %2 = "tf.ExpandDims"(%1, %const_0) : (tensor<10000xi32>, tensor<i32>) -> tensor<1x10000xi32>
  %3 = "tf.Tile"(%2, %const_64_1) : (tensor<1x10000xi32>, tensor<2xi32>) -> tensor<64x10000xi32>
  // CHECK-NOT: tf.Range
  // CHECK-NOT: tf.ExpandDims

      tf_device.return %5#0, %5#1, %3 : tensor<?x300xi32>, tensor<?xi32>, tensor<?x4x!tf_type.string>
    }) {device_assignment = [], num_cores_per_replica = 1 : i64, topology = ""} : () -> (tensor<?x300xi32>, tensor<?xi32>, tensor<?x4x!tf_type.string>)
    return %0#0, %0#1, %0#2 : tensor<?x300xi32>, tensor<?xi32>, tensor<?x4x!tf_type.string>
  }
}

  %r3 = "tf.Reshape"(%arg2, %shape1) : (tensor<10000xf32>, tensor<2xi32>) -> tensor<100x100xf32>
  %shape3 = arith.constant dense<[-1, 100]> : tensor<2xi32>
  %r4 = "tf.Reshape"(%arg2, %shape3) : (tensor<10000xf32>, tensor<2xi32>) -> tensor<100x100xf32>
  %r5 = "tf.Reshape"(%arg0, %arg3) : (tensor<*xf32>, tensor<*xi32>) -> tensor<*xf32>
  %r6 = "tf.Reshape"(%arg2, %arg3) : (tensor<10000xf32>, tensor<*xi32>) -> tensor<*xf32>

	{"2001:db8::1/-1", nil, nil, &ParseError{Type: "CIDR address", Text: "2001:db8::1/-1"}},
	{"2001:db8::1/-0", nil, nil, &ParseError{Type: "CIDR address", Text: "2001:db8::1/-0"}},
	{"-0.0.0.0/32", nil, nil, &ParseError{Type: "CIDR address", Text: "-0.0.0.0/32"}},
	{"0.-1.0.0/32", nil, nil, &ParseError{Type: "CIDR address", Text: "0.-1.0.0/32"}},