%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>

    ^bb0(%arg1: tensor<f32>, %arg2: tensor<f32>):
      %2 = stablehlo.maximum %arg1, %arg2 : tensor<f32>
      stablehlo.return %2 : tensor<f32>
  }) {padding = dense<[[0, 0], [1, 1], [1, 1], [0, 0]]> : tensor<4x2xi64>, window_dimensions = array<i64: 1, 3, 3, 1>} : (tensor<2x3x1x3xf32>, tensor<f32>) -> tensor<2x3x1x3xf32>
  return %1 : tensor<2x3x1x3xf32>
}

// -----

    func.func @main() -> () {
      %cst0 = "tf.Const"(){ value = dense<0> : tensor<3x5xi1>} : () -> tensor<3x5xi1>
      %0 = "tf.Where"(%cst0) : (tensor<3x5xi1>) -> tensor<?x2xi64>
      func.return
    }
  })";

  CellReader<int64_t> dynamism_counter(kDynamismOpCounterStreamzName);
  CellReader<int64_t> dynamism_function_counter(
      kDynamismFunctionCounterStreamzName);

func.func @main(%arg0: tensor<10x8x9xf32>) -> tensor<2xi64> {
  %elem_shape = "tf.Const"() {value = dense<[8, 9]> : tensor<2xi32>} : () -> tensor<2xi32>
  %tl = "tf.TensorListFromTensor"(%arg0, %elem_shape) : (tensor<10x8x9xf32>, tensor<2xi32>) -> tensor<!tf_type.variant<tensor<8x9xf32>>>
  // CHECK: %[[SHAPE:.*]] = "tf.Const"() <{value = dense<[8, 9]> : tensor<2xi64>}> : () -> tensor<2xi64>

  ^bb0(%arg1: tensor<f32>, %arg2: tensor<f32>):
    %7 = stablehlo.maximum %arg1, %arg2 : tensor<f32>
    stablehlo.return %7 : tensor<f32>
  }) {padding = dense<[[0, 0], [0, 1], [0, 1], [0, 0]]> : tensor<4x2xi64>, window_dimensions = array<i64: 1, 3, 3, 1>, window_strides = array<i64: 1, 2, 2, 1>} : (tensor<?x112x112x64xf32>, tensor<f32>) -> tensor<?x56x56x64xf32>
  return %6 : tensor<?x56x56x64xf32>

//   [counter_lo, counter_hi, key]
//
//   RngReadAndSkip increments the 128 bit counter value by 256 * delta and
//   returns the original state value.
//
// For Threefry, the resource variable holds a tensor<2xi64> with the state:
//   [counter, key]
//
//   RngReadAndSkip increments the 64 bit counter value by 256 * delta and
//   returns a tensor<3xi64> value [counter, key, 0].
class DecomposeRngReadAndSkipOp : public RewritePattern {

      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>