%cst_0 = arith.constant dense<[2, -1]> : tensor<2xi64>
  %0 = "tf.FakeQuantWithMinMaxVars"(%arg0, %arg1, %arg2) {num_bits = 5, narrow_range = false} : (tensor<1x2xf32>, tensor<f32>, tensor<f32>) -> tensor<1x2xf32>
  %1 = "tf.Reshape"(%0, %cst_0) : (tensor<1x2xf32>, tensor<2xi64>) -> tensor<2x1xf32>
  %2 = "tf.Reshape"(%0, %cst_0) : (tensor<1x2xf32>, tensor<2xi64>) -> tensor<2x1xf32>
  func.return %1, %2 : tensor<2x1xf32>, tensor<2x1xf32>

  %cst_0 = arith.constant dense<[2, -1]> : tensor<2xi64>
  %0 = "tf.FakeQuantWithMinMaxVars"(%arg0, %arg1, %arg2) {num_bits = 3, narrow_range = false} : (tensor<1x2xf32>, tensor<f32>, tensor<f32>) -> tensor<1x2xf32>
  %1 = "tf.Reshape"(%0, %cst_0) : (tensor<1x2xf32>, tensor<2xi64>) -> tensor<2x1xf32>
  %2 = "tf.Reshape"(%0, %cst_0) : (tensor<1x2xf32>, tensor<2xi64>) -> tensor<2x1xf32>
  func.return %1, %2 : tensor<2x1xf32>, tensor<2x1xf32>

      stablehlo.return %3 : tensor<f32>
  }) {
    window_dimensions = array<i64: 1, 1, 2, 2>,
    window_strides = array<i64: 1, 1, 2, 2>,
    padding = dense<[[0, 0], [0, 0], [1, 1], [1, 1]]> : tensor<4x2xi64>
  } : (tensor<1x4x16x16xf32>, tensor<f32>) -> tensor<1x4x9x9xf32>
  return %2 : tensor<1x4x9x9xf32>
}
// CHECK-SAME: %[[ARG:.+]]: tensor<1x16x16x4xf32>
// CHECK-DAG: stablehlo.constant

// 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

//        limit_indices = dense<[4, 1]> : tensor<2xi64>,
//        start_indices = dense<0> : tensor<2xi64>,
//        strides = dense<1> : tensor<2xi64>} :
//        (tensor<4x6xf32>) -> tensor<4x1xf32>
// %1 = "mhlo.slice"(%input) {
//        limit_indices = dense<[4, 3]> : tensor<2xi64>,
//        start_indices = dense<[0, 1]> : tensor<2xi64>,
//        strides = dense<1> : tensor<2xi64>} :
//        (tensor<4x6xf32>) -> tensor<4x2xf32>

// CHECK-DAG:       [[VAL_35:%.*]] = arith.constant dense<0> : tensor<1xi64>
// CHECK-DAG:       [[VAL_36:%.*]] = arith.constant dense<1> : tensor<1xi64>
// CHECK:           [[VAL_37:%.*]] = "tf.Slice"([[VAL_3]], [[VAL_35]], [[VAL_36]]) : (tensor<2xf32>, tensor<1xi64>, tensor<1xi64>) -> tensor<1xf32>
// CHECK-DAG:       [[VAL_38:%.*]] = arith.constant dense<1> : tensor<1xi64>

}

// CHECK-LABEL: replaceReshapeEqualWithOneHot
func.func @replaceReshapeEqualWithOneHot(%arg: tensor<2x1xi32>) -> tensor<2x3xi1> {
  // Good match: Replace with one_hot
  %cst = arith.constant dense<[0, 1, 2]> : tensor<3xi32>
  %result = "tfl.equal"(%arg, %cst) : (tensor<2x1xi32>, tensor<3xi32>) -> tensor<2x3xi1>
  func.return %result : tensor<2x3xi1>

// CHECK: %[[BITCAST2:.+]] = "tfl.bitcast"(%[[ARG2]]) : (tensor<i64>) -> tensor<1xi64>
// CHECK: %[[MIN2:.+]] = "tfl.minimum"(%[[BITCAST2]], %[[MAX2]]) : (tensor<1xi64>, tensor<1xi64>) -> tensor<1xi64>
// CHECK: %[[CONCAT:.+]] = "tfl.concatenation"(%[[MIN1]], %[[MIN2]]) <{axis = 0 : i32, fused_activation_function = "NONE"}> : (tensor<1xi64>, tensor<1xi64>) -> tensor<2xi64>

// CHECK:  %[[cst:.*]] = "tfl.pseudo_qconst"() <{qtype = tensor<2x4x!quant.uniform<i4:f32, 2.500000e-01:-1>>, value = dense<{{\[\[}}-4, -3, -2, -1{{\]}}, [0, 1, 2, 3{{\]\]}}> : tensor<2x4xi4>}>
// CHECK:  return %[[cst]]
}

// CHECK-LABEL: QuantizeDenseFloatConst
func.func @QuantizeDenseFloatConst() -> tensor<2x2x!quant.uniform<u8:f32, 7.8431372549019615E-4:128>> {