// CHECK-SAME: <{calibration_methods = [1 : i32, 1 : i32], ids = ["0", "1"], output_file_path = "main_0.pb"}> : (tensor<f32>, tensor<f32>, tensor<0xi64>, tensor<f32>, tensor<f32>, tensor<0xi64>) -> ()
  // CHECK: return

  %2 = mhlo.constant dense<3.40282347E+38> : tensor<f32>
  %3 = "mhlo.broadcast_in_dim"(%2) <{broadcast_dimensions = dense<> : tensor<0xi64>}> : (tensor<f32>) -> tensor<2xf32>
  %4 = mhlo.constant dense<3.000000e+00> : tensor<f32>
  %5 = "mhlo.broadcast_in_dim"(%4) <{broadcast_dimensions = dense<> : tensor<0xi64>}> : (tensor<f32>) -> tensor<2xf32>

// MIN-MAX-CHECK-NEXT:  [[lhs:%.*]], {{.*}}, {{.*}}, {{.*}} = "tf.CustomAggregator"(%arg0) <{calibration_method = 1 : i32, id = "composite_conv2d_with_relu6_fn_arg_0_calibration_method_1", max_percentile = 0.000000e+00 : f32, min_percentile = 0.000000e+00 : f32, num_bins = 0 : i32}> : (tensor<*xf32>) -> (tensor<*xf32>, tensor<f32>, tensor<f32>, tensor<0xi64>)

func.func @slice_variable_start(%arg0: tensor<3x4xi32>, %arg1: tensor<2xi64>) -> tensor<1x4xi32> {
  // CHECK: %[[SLICED_START1:.*]] = "mhlo.slice"(%arg1)
  // CHECK-DAG-SAME: {limit_indices = dense<1> : tensor<1xi64>,
  // CHECK-DAG-SAME: start_indices = dense<0> : tensor<1xi64>,
  // CHECK-DAG-SAME: strides = dense<1> : tensor<1xi64>} : (tensor<2xi64>) -> tensor<1xi64>

    func.func @main(%arg0: tensor<f32>) {
      %elem = "tf.Const"() <{value = dense<0.000000e+00> : tensor<f32>}> : () -> tensor<f32>
      %shape = "tf.Const"() <{value = dense<> : tensor<0xi64>}> : () -> tensor<0xi64>
      %zero = "tf.Fill"(%shape, %elem) : (tensor<0xi64>, tensor<f32>) -> tensor<f32>
      tf_device.replicate([%arg0, %zero] as %arg1: tensor<f32>) {n = 2 : i32} {
        %arg1_id = "tf_device.launch"() <{device = "TPU_REPLICATED_HOST_0"}> ({

  // CHECK-DAG: %[[X_NORMED:.+]] = mhlo.multiply %[[X]], %[[MULTIPLIER_BCAST]] : tensor<4x256xf32>
  // CHECK-DAG: %[[RHS_BCAST:.+]] = "mhlo.broadcast_in_dim"(%[[RHS]]) <{broadcast_dimensions = dense<1> : tensor<1xi64>}> : (tensor<256xf32>) -> tensor<4x256xf32>

    // CHECK: "mhlo.pad"(%[[ARG0]], %[[ARG1]])
    // CHECK-SAME-DAG: edge_padding_high = dense<[1, 2]> : tensor<2xi64>
    // CHECK-SAME-DAG: edge_padding_low = dense<[2, 1]> : tensor<2xi64>
    // CHECK-SAME-DAG: interior_padding = dense<[1, 0]> : tensor<2xi64>

    %0 = mhlo.constant dense<[2, 1]> : tensor<2xi32>
    %1 = mhlo.constant dense<[1, 2]> : tensor<2xi32>

	CALC_F3_PRE(0x14c,AX) \
	PRECALC_19(Y7) \
	CALC_F3_POST(DX,BX,SI,AX,CX)

#define CALC_44 \
	CALC_F3_PRE(0x160,DI) \
	PRECALC_20(Y7) \
	CALC_F3_POST(AX,CX,BX,DI,DX)

#define CALC_45 \
	CALC_F3_PRE(0x164,SI) \
	PRECALC_21(Y7) \
	CALC_F3_POST(DI,DX,CX,SI,AX)

#define CALC_46 \
	CALC_F3_PRE(0x168,BX) \
	CALC_F3_POST(SI,AX,DX,BX,DI)

#define CALC_47 \
	CALC_F3_PRE(0x16c,CX) \
	VPXOR Y9, Y0, Y7 \

DATA K256<>+0x150(SB)/4, $0xa2bfe8a1
DATA K256<>+0x154(SB)/4, $0xa81a664b
DATA K256<>+0x158(SB)/4, $0xc24b8b70
DATA K256<>+0x15c(SB)/4, $0xc76c51a3

DATA K256<>+0x160(SB)/4, $0xd192e819
DATA K256<>+0x164(SB)/4, $0xd6990624
DATA K256<>+0x168(SB)/4, $0xf40e3585
DATA K256<>+0x16c(SB)/4, $0x106aa070
DATA K256<>+0x170(SB)/4, $0xd192e819
DATA K256<>+0x174(SB)/4, $0xd6990624
DATA K256<>+0x178(SB)/4, $0xf40e3585