} : (tensor<2x2x!tf_type.qint8>, tensor<2xf32>, tensor<2xi32>, tensor<2xf32>, tensor<2xi32>) -> tensor<2x2x!tf_type.qint8>
  %2 = "tf.UniformDequantize"(%1, %scales_1, %zps_1) {
    quantization_axis = 0 : i64, quantization_min_val = -128 : i64, quantization_max_val = 127 : i64
  } : (tensor<2x2x!tf_type.qint8>, tensor<2xf32>, tensor<2xi32>) -> tensor<2x2xf32>
  func.return %2 : tensor<2x2xf32>
}

// CHECK-DAG: %[[CAST_0:.*]] = "tf.Cast"(%[[ARG_0]]) <{Truncate = false}> : (tensor<2xbf16>) -> tensor<2xf32>
// CHECK-DAG: %[[CAST_1:.*]] = "tf.Cast"(%[[ARG_1]]) <{Truncate = false}> : (tensor<2xbf16>) -> tensor<2xf32>
// CHECK: %[[ADD:.*]] = "tf.AddV2"(%[[CAST_0]], %[[CAST_1]]) : (tensor<2xf32>, tensor<2xf32>) -> tensor<2xf32>

    tf_executor.graph {
      %out, %ctl = tf_executor.island wraps "tf.VarHandleOp"() {shared_name = "var_0"} : () -> tensor<!tf_type.resource<tensor<2xf32>>>
      %out_0, %ctl_0 = tf_executor.island wraps "tf.ReadVariableOp"(%out) : (tensor<!tf_type.resource<tensor<2xf32>>>) -> tensor<2xf32>
      %out_1, %ctl_1 = tf_executor.island wraps "tf.Const"() {value = dense<"var_0"> : tensor<1x!tf_type.string>} : () -> tensor<1x!tf_type.string>

    %cst_0 = "tf.Const"() {value = dense<1.000000e+00> : tensor<2xf32>} : () -> tensor<2xf32>
    %var_0 = "tf.VarHandleOp"() {shared_name = "var_0"} : () -> tensor<!tf_type.resource<tensor<2xf32>>>
    "tf.AssignVariableOp"(%var_0, %cst_0) : (tensor<!tf_type.resource<tensor<2xf32>>>, tensor<2xf32>) -> ()
    %add_0 = "tf.Identity"(%cst_0) : (tensor<2xf32>) -> tensor<2xf32>

func.func private @sum_reducer(%arg0: tensor<*xf32>, %arg1: tensor<*xf32>) -> tensor<*xf32> {
  %0 = "tf.AddV2"(%arg0, %arg1) {device = ""} : (tensor<*xf32>, tensor<*xf32>) -> tensor<*xf32>
  func.return %0 : tensor<*xf32>
}

func.func @xla_func_0(%arg0: tensor<7xf32>, %arg1: tensor<f32>) -> tensor<10xf32> {
  %cst = "tf.Const"() {value = dense<0> : tensor<1x2xi32>} : () -> tensor<1x2xi32>

    %3 = shape.shape_of %2 : tensor<?x2xf32> -> tensor<2xindex>
    %4 = stablehlo.dynamic_broadcast_in_dim %arg2, %3, dims = [1] : (tensor<2xf32>, tensor<2xindex>) -> tensor<?x2xf32>
    %5 = stablehlo.add %2, %4 : tensor<?x2xf32>
    %6 = stablehlo.clamp %0, %5, %1 : (tensor<f32>, tensor<?x2xf32>, tensor<f32>) -> tensor<?x2xf32>
    return %6 : tensor<?x2xf32>
  }

func.func @main(
    %arg0: tensor<10x10xf32>, %scale: tensor<10xf32>, %zp: tensor<10xi32>
  ) -> tensor<10x10xi8> {
  %0 = "tf.UniformQuantize"(%arg0, %scale, %zp) {
    quantization_axis = 1 : i64,
    quantization_min_val = -128 : i64,
    quantization_max_val = 127 : i64
  } : (tensor<10x10xf32>, tensor<10xf32>, tensor<10xi32>) -> tensor<10x10x!tf_type.qint8>

      Targs = [tensor<32xf32>, tensor<64xf32>, tensor<8xf32>, tensor<2xf32>],
      Tresults = [tensor<32xf32>, tensor<64xf32>, tensor<8xf32>]}
      : (tensor<32xf32>, tensor<64xf32>, tensor<8xf32>, tensor<2xf32>, tensor<2x!tf_type.string>)
        -> (tensor<32xf32>, tensor<64xf32>, tensor<8xf32>)
    tf_device.return %0#0, %0#1, %0#2 : tensor<32xf32>, tensor<64xf32>, tensor<8xf32>

  %biasadd = "tf.BiasAdd"(%conv, %dq_bias) {data_format = "NHWC", device = ""} : (tensor<*xf32>, tensor<2xf32>) -> tensor<*xf32>
  %res = "tf.Relu6"(%biasadd) : (tensor<*xf32>) -> tensor<*xf32>
  %q_res = "quantfork.qcast"(%res) : (tensor<*xf32>) -> tensor<*x!quant.uniform<i8:f32, 0.023529411764705882:-128>>

    %cst_1 = "tf.Const"() {value = dense<3.000000e+00> : tensor<2x3x3x512xf32>} : () -> tensor<2x3x3x512xf32>
    %0 = "tf.PartitionedCall"(%arg0, %cst_1) {_tfl_quant_trait = "fully_quantizable", config = "", config_proto = "", executor_type = "", f = @composite_depthwise_conv2d_fn} : (tensor<1x3x4x512xf32>, tensor<2x3x3x512xf32>) -> tensor<*xf32>