%3 = tfrt_fallback_async.executeop key(1) cost(512) device("/job:localhost/replica:0/task:0/device:CPU:0") "tf.AddV2"(%0, %0) {T = f32} : 1
  %4 = tfrt_fallback_async.executeop key(1) cost(512) device("/job:localhost/replica:0/task:0/device:CPU:0") "tf.AddV2"(%0, %0) {T = f32} : 1
  %5 = tfrt_fallback_async.executeop key(1) cost(512) device("/job:localhost/replica:0/task:0/device:CPU:0") "tf.AddV2"(%0, %0) {T = f32} : 1

// CHECK: return %[[CONV2D]] : tensor<1x3x3x2x!quant.uniform<i8:f32, 4.000000e+00>>

// -----

func.func @convolution_upstream_srq_non_const_filter(%arg0: tensor<1x3x3x4x!quant.uniform<i8:f32, 1.000000e+0:-100>>, %arg1: tensor<3x3x4x2x!quant.uniform<i8:f32:3, {2.000000e+2, 3.000000e+3}>>) -> tensor<1x3x3x2x!quant.uniform<i8:f32, 4.000000e+0>> {

  %requant = stablehlo.uniform_quantize %conv : (tensor<1x3x4x2x!quant.uniform<i32:f32, 5.8949912267181218E-5>>) -> tensor<1x3x4x2x!quant.uniform<i8:f32, 0.045673100153605144:-62>>

        %1 = "tf.opB"() : () -> tensor<f32>
        tf_executor.yield %1 : tensor<f32>
      }
      tf_executor.fetch %outputs_0 : tensor<f32>
    }
    func.return %0 : tensor<f32>
  }
// CHECK-LABEL: func @func2
  func.func @func2(%arg0: tensor<i1>) -> tensor<i1> {
    %0 = tf_executor.graph {
      %outputs, %control = tf_executor.island {
        %1 = "tf.opB"() : () -> tensor<f32>
        tf_executor.yield %1 : tensor<f32>

  %w = "tfl.pseudo_qconst"() {qtype = tensor<12x2x!quant.uniform<i8<-127:127>:f32, 1.000000e+00>>, value = dense<127> : tensor<12x2xi8>} : () -> tensor<12x2x!quant.uniform<i8<-127:127>:f32, 1.000000e+00>>
  %dq_w = "tfl.dequantize"(%w) : (tensor<12x2x!quant.uniform<i8<-127:127>:f32, 1.000000e+00>>) -> tensor<12x2xf32>

}

func.func @branch0(%arg0: tensor<f32>, %arg1: tensor<f32>) -> tensor<f32> {
  %0 = "tf.Add" (%arg0, %arg1) {device = "/device:CPU:0"}  : (tensor<f32>, tensor<f32>) -> tensor<f32>
  func.return %0 : tensor<f32>
}

func.func @branch1(%arg0: tensor<f32>, %arg1: tensor<f32>) -> tensor<f32> {
  %0 = "tf.Add" (%arg0, %arg1) {device = "/device:CPU:0"}  : (tensor<f32>, tensor<f32>) -> tensor<f32>

  %cst = "tf.Const"() {value = dense<-0.950868546> : tensor<f32>} : () -> tensor<f32>
  %cst_0 = "tf.Const"() {value = dense<9.951540e-01> : tensor<f32>} : () -> tensor<f32>
  %cst_1 = "tf.Const"() {value = dense<[-0.5, -0.4, -0.7]> : tensor<3xf32>} : () -> tensor<3xf32>
  %cst_2 = "tf.Const"() {value = dense<[0.5, 0.6, 0.3]> : tensor<3xf32>} : () -> tensor<3xf32>

@testUnidirectionalSequenceLstmWithIntermediates(%arg0: tensor<? x ? x ? x f32>, %arg1: tensor<? x ? x f32>, %arg2: tensor<? x ? x f32>, %arg3: tensor<? x ? x f32>, %arg4: tensor<? x ? x f32>, %arg5: tensor<? x ? x f32>, %arg6: tensor<? x ? x f32>, %arg7: tensor<? x ? x f32>, %arg8: tensor<? x ? x f32>, %arg9: tensor<? x f32>, %arg10: tensor<? x f32>, %arg11: tensor<? x f32>, %arg12: tensor<? x f32>, %arg13: tensor<? x f32>, %arg14: tensor<? x f32>, %arg15: tensor<? x f32>, %arg16: tensor<? x ? x f32>, %arg17:...

  %4 = "tfl.reshape"(%3, %cst) : (tensor<1x112x112x32x!quant.uniform<u8:f32, 0.023528476789885875>>, tensor<2xi32>) -> tensor<1x401408x!quant.uniform<u8:f32, 0.023528476789885875>>
  %5 = "tfl.softmax"(%4) {beta = 1.000000e+00 : f32} : (tensor<1x401408x!quant.uniform<u8:f32, 0.023528476789885875>>) -> tensor<1x401408x!quant.uniform<u8:f32, 3.906250e-03>>

// WHOLE-PASSES-DAG:  %[[rv2:.*]] = "tfl.read_variable"({{.*}}) : (tensor<*x!tf_type.resource<tensor<1x2x3x!quant.uniform<u8:f32, {{.*}}>>>>) -> tensor<1x2x3x!quant.uniform<u8:f32, {{.*}}>>