%9 = "mhlo.compare"(%0, %8) {comparison_direction = #mhlo<comparison_direction NE>} : (tensor<10x8xf32>, tensor<10x8xf32>) -> tensor<10x8xi1>
  %10 = mhlo.and %7, %9 : tensor<10x8xi1>
  %11 = mhlo.subtract %arg0, %6 : tensor<10x8xf32>
  %12 = mhlo.divide %11, %5 : tensor<10x8xf32>
  %13 = mhlo.add %12, %3 : tensor<10x8xf32>

}

// -----

// CHECK-LABEL: func @select_batch_static_r1
func.func @select_batch_static_r1(%arg0: tensor<i1>, %arg1: tensor<2x6x8xi32>, %arg2: tensor<2x6x8xi32>) -> tensor<2x6x8xi32> {
  // CHECK: mhlo.select %arg0, %arg1, %arg2
  %0 = "tf.Select"(%arg0, %arg1, %arg2) : (tensor<i1>, tensor<2x6x8xi32>, tensor<2x6x8xi32>) -> tensor<2x6x8xi32>
  func.return %0: tensor<2x6x8xi32>
}

// -----

  // time constant.
  %0 = "tf.ConcatV2"(%arg0, %arg0, %arg0, %arg0, %axis) : (tensor<16x1xf32>, tensor<16x1xf32>, tensor<16x1xf32>, tensor<16x1xf32>, tensor<i32>) -> tensor<16x4xf32>

  // Just to introduce an extra use for %cst.
  %1 = "tf.AddV2"(%axis, %axis) {device = ""} : (tensor<i32>, tensor<i32>) -> tensor<i32>

  return %0 : tensor<16x4xf32>
}
// Check that the constant is cloned with same value.

  // CHECK: return %[[RESULT]] : tensor<1x4xi32>
  %sizes = "tf.Const"() {value = dense<[1, 4]> : tensor<2xi64>} : () -> (tensor<2xi64>)
  %0 = "tf.Slice"(%arg0, %arg1, %sizes) : (tensor<3x4xi32>, tensor<2xi64>, tensor<2xi64>) -> tensor<1x4xi32>
  func.return %0 : tensor<1x4xi32>
}

// CHECK-LABEL: slice_variable_start_negsize

  %split_dim_2 = arith.constant dense<1> : tensor<1xi32>
  %4, %5 = "tfl.split"(%split_dim_2, %arg0) {num_splits = 2 : i32} : (tensor<1xi32>, tensor<16x4xf32>) -> (tensor<16x2xf32>, tensor<16x2xf32>)
  %6:2 = "tfl.split"(%split_dim_2, %arg0) {num_splits = 2 : i32} : (tensor<1xi32>, tensor<16x4xf32>) -> (tensor<16x2xf32>, tensor<16x?xf32>)

func.func @reshape_vector_shape(tensor<4x4x4xf32>) -> tensor<16x4xf32> {
^bb0(%arg0: tensor<4x4x4xf32>) :
  %shape0 = arith.constant dense<[[16, 4]]> : tensor<1x2xi32>
  // expected-error @+1 {{'tfl.reshape' op requires 'shape' to be rank 1, but got 2}}
  %1 = "tfl.reshape"(%arg0, %shape0) : (tensor<4x4x4xf32>, tensor<1x2xi32>) -> tensor<16x4xf32>
  func.return %1 : tensor<16x4xf32>
}

// -----

  // CHECK-SAME: -> tensor<!tf_type.variant<tensor<16x1xf32>>>
  func.func @while_variant(%arg0: tensor<!tf_type.variant<tensor<16x1xf32>>>) -> tensor<!tf_type.variant> {
    // CHECK: tf.While
    // CHECK-SAME: -> tensor<!tf_type.variant<tensor<16x1xf32>>>
    %0 = "tf.While"(%arg0) {cond = @variant_cond_func, body = @variant_body_func, is_stateless = true} : (tensor<!tf_type.variant<tensor<16x1xf32>>>) -> tensor<!tf_type.variant>

  // CHECK-NEXT: %[[UPDATE:.*]] = "tf.XlaDynamicUpdateSlice"(%[[STACK_VAL]], %[[UPDATE_SLICE]], %[[CONCAT_OFFETS]]) : (tensor<10x2xi32>, tensor<1x2xi32>, tensor<2xi32>) -> tensor<10x2xi32>
  // CHECK-NEXT: "tf.AssignVariableOp"(%[[BUFFER]], %[[UPDATE]]) : (tensor<!tf_type.resource<tensor<10x2xi32>>>, tensor<10x2xi32>) -> ()
  // CHECK-NEXT: %[[CONST1:.*]] = "tf.Const"() <{value = dense<1> : tensor<1xi32>}> : () -> tensor<1xi32>

    %8 = stablehlo.convert %7 : (tensor<1x2xi32>) -> tensor<1x2xf32>
    %9 = stablehlo.convert %2 : (tensor<2x3xi8>) -> tensor<2x3xf32>
    %10 = stablehlo.dot_general %8, %9, contracting_dims = [1] x [0] : (tensor<1x2xf32>, tensor<2x3xf32>) -> tensor<1x3xf32>
    %11 = stablehlo.convert %3 : (tensor<1x3xi32>) -> tensor<1x3xf32>

    %0 = "tf.Const"() {value = dense<1> : tensor<i64>} : () -> tensor<i64>
    %1 = "tf.Const"() {value = dense<0> : tensor<1x1xi32>} : () -> tensor<1x1xi32>
    %2 = "tf.Const"() {value = dense<[7, 7, 3, 64]> : tensor<4xi32>} : () -> tensor<4xi32>
    %3 = "tf.Const"() {value = dense<[[0, 0], [3, 3], [3, 3], [0, 0]]> : tensor<4x2xi32>} : () -> tensor<4x2xi32>