%0 = "tf.Sin"(%arg0) : (tensor<?xf32>) -> tensor<?xf32>
  func.return %0 : tensor<?xf32>
}

// -----

// CHECK-LABEL: func @rsqrt
func.func @rsqrt(%arg0: tensor<2xf32>) -> tensor<2xf32> {
  // CHECK:  mhlo.rsqrt %arg0 : tensor<2xf32>
  %0 = "tf.Rsqrt"(%arg0) : (tensor<2xf32>) -> tensor<2xf32>
  func.return %0 : tensor<2xf32>
}

// -----

// CHECK-LABEL: func @rsqrt_dynamic

}

// CHECK-LABEL:   func @rsqrt(
// CHECK-SAME:                %[[VAL_0:.*]]: tensor<2xf32>) -> tensor<2xf32> {
// CHECK:           %[[VAL_1:.*]] = "tf.Rsqrt"(%[[VAL_0]]) : (tensor<2xf32>) -> tensor<2xf32>
// CHECK:           return %[[VAL_1]] : tensor<2xf32>
// CHECK:         }
func.func @rsqrt(%arg0: tensor<2xf32>) -> tensor<2xf32> {
  %0 = "mhlo.rsqrt"(%arg0) : (tensor<2xf32>) -> tensor<2xf32>

  %cst = arith.constant dense<[0]> : tensor<1xi32>
  %0 = "tfl.square"(%arg0) : (tensor<2xf32>) -> tensor<2xf32>
  %1 = "tfl.sum"(%0, %cst) {keep_dims = false} : (tensor<2xf32>, tensor<1xi32>) -> tensor<f32>
  %2 = "tfl.rsqrt"(%1) : (tensor<f32>) -> tensor<f32>
  %3 = "tfl.mul"(%arg0, %2) {fused_activation_function = "NONE"} : (tensor<2xf32>, tensor<f32>) -> tensor<2xf32>
  func.return %3: tensor<2xf32>

expression:
</p>

<pre>
var a, b, c = f() + v(), g(), sqr(u()) + v()

func f() int        { return c }
func g() int        { return a }
func sqr(x int) int { return x*x }

// functions u and v are independent of all other variables and functions
</pre>

<p>
The function calls happen in the order
<code>u()</code>, <code>sqr()</code>, <code>v()</code>,

        non_feature_dims.push_back(i);
      }
      auto reduce_dims = GetI64ElementsAttr(non_feature_dims, &rewriter);
      auto scalar_broadcast_dims = rewriter.getDenseI64ArrayAttr({});

      // scratch1 = rsqrt(var + epsilon)
      RankedTensorType scalar_float =
          tensorflow::GetTypeFromTFTensorShape({}, kernel_type);
      auto epsilon = rewriter.create<ConstantOp>(

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