// CHECK-LABEL: testAddWithI64Broadcasting
func.func @testAddWithI64Broadcasting(tensor< 2x3xi64>, tensor<3xi64>) -> tensor<2x3xi64> {
^bb0(%arg0: tensor<2x3xi64>, %arg1: tensor<3xi64>):
  // CHECK: tfl.add(%arg0, %arg1)
  %0 = tfl.add(%arg0, %arg1) {fused_activation_function = "RELU6"} : (tensor< 2x3xi64>, tensor<3xi64>) -> tensor<2x3xi64>
  func.return %0#0 : tensor<2x3xi64>
}

// -----

// CHECK-LABEL: add_with_i32_five_dim_broadcasting

// CHECK:         }
func.func @concat_v2(%arg0: tensor<3x3xf32>, %arg1: tensor<3x3xf32>) -> tensor<6x3xf32> {
  %2 = "mhlo.concatenate"(%arg0, %arg1) <{dimension = 0 : i64}> : (tensor<3x3xf32>, tensor<3x3xf32>) -> tensor<6x3xf32>
  func.return %2 : tensor<6x3xf32>
}

// CHECK-LABEL:   func @concat_v2_1d_axis(
// CHECK-SAME:                            %[[VAL_0:.*]]: tensor<3x3xf32>,

  %1 = "tfl.dequantize"(%arg1) : (tensor<3x3x!quant.uniform<u8<1:255>:f32, 1.0>>) -> tensor<3x3xf32>
  %2 = "tfl.fully_connected"(%0, %1, %arg2) {fused_activation_function = "NONE", keep_num_dims = false, weights_format = "DEFAULT"} : (tensor<3xf32>, tensor<3x3xf32>, none) -> tensor<3xf32>

  // CHECK-DAG: [[VAL1:%.+]] = "tf.Const"() <{value = dense<[1, 5, 3, 2]> : tensor<4xi64>}>
  // CHECK-DAG: [[VAL2:%.+]] = "tf.Const"() <{value = dense<{{\[\[}}0, 0], [3, 4], [0, 0{{\]\]}}> : tensor<3x2xi64>}>
  // CHECK-DAG: [[VAL3:%.+]] = "tf.Const"() <{value = dense<0.000000e+00> : tensor<f32>}>
  // CHECK-DAG: [[VAL4:%.+]] = "tf.Const"() <{value = dense<[2, 0, 1, 3]> : tensor<4xi64>}>

func.func @testIncompatibleElementTypes(%arg0: tensor<3x2xf32>, %arg1: tensor<3x2xf32>) -> (tensor<3x2xf64>) {
    // expected-error @+1 {{'tf.Mul' op requires compatible element types for all operands and results}}
  %0 = "tf.Mul"(%arg0, %arg1) : (tensor<3x2xf32>, tensor<3x2xf32>) -> tensor<3x2xf64>
  func.return %0 : tensor<3x2xf64>
}

// -----

func.func @testPadRank1Paddings(%input: tensor<2xi64>) -> tensor<3xi64> {

// `tfl.dequantize`.

func.func @uniform_dequantize_op_return_f64(%arg: tensor<2x2x!quant.uniform<i8:f64, 1.000000e+0:8>>) -> tensor<2x2xf64> {
  %0 = stablehlo.uniform_dequantize %arg : (tensor<2x2x!quant.uniform<i8:f64, 1.000000e+0:8>>) -> tensor<2x2xf64>
  return %0 : tensor<2x2xf64>
}
// CHECK-LABEL: uniform_dequantize_op_return_f64
// CHECK: stablehlo.uniform_dequantize
// CHECK-NOT: tfl.dequantize

// -----

	0x3f52: 0x000a, 0x3f53: 0x000a, 0x3f54: 0x000a, 0x3f55: 0x000a, 0x3f56: 0x000a, 0x3f57: 0x000a,
	0x3f58: 0x000a, 0x3f59: 0x000a, 0x3f5a: 0x000a, 0x3f5b: 0x000a,
	0x3f60: 0x000a, 0x3f61: 0x000a, 0x3f62: 0x000a, 0x3f63: 0x000a,
	0x3f64: 0x000a, 0x3f65: 0x000a, 0x3f66: 0x000a, 0x3f67: 0x000a, 0x3f68: 0x000a,
	0x3f70: 0x000a, 0x3f71: 0x000a, 0x3f72: 0x000a, 0x3f73: 0x000a, 0x3f74: 0x000a, 0x3f75: 0x000a,
	0x3f76: 0x000a, 0x3f77: 0x000a, 0x3f78: 0x000a,