%4 = "stablehlo.reduce_window"(%3, %5) ({  // max pool
  ^bb0(%arg1: tensor<f32>, %arg2: tensor<f32>):
      %6 = stablehlo.maximum %arg1, %arg2 : tensor<f32>
      stablehlo.return %6 : tensor<f32>
  }) {
    window_dimensions = array<i64: 1, 1, 2, 2>,
    window_strides = array<i64: 1, 1, 2, 2>
  } : (tensor<1x4x5x5xf32>, tensor<f32>) -> tensor<1x4x2x2xf32>
  return %4 : tensor<1x4x2x2xf32>
}

    %cst_0 = arith.constant dense<1> : tensor<256x256xi8>
    %cst_1 = "tfl.pseudo_qconst"() {qtype = tensor<256x256x!quant.uniform<i8:f32, 1.000000e+00>>, value = dense<1>  : tensor<256x256xi8>} : () -> tensor<256x256x!quant.uniform<i8:f32, 1.000000e+00>>
    %cst_2 = arith.constant dense<0> : tensor<i32>

# MLIR-SAME:  control_outputs = ""
# MLIR-SAME:  inputs = "input"
# MLIR-SAME:  outputs = "output"
# MLIR:         %[[shape:.*]] = arith.constant dense<[1, -1, 31]> : tensor<3xi32>
# MLIR:         %[[bias:.*]] = "tfl.pseudo_qconst"() <{qtype = tensor<186x!quant.uniform<i32:f32:0

# MLIR-SAME:  control_outputs = ""
# MLIR-SAME:  inputs = "input"
# MLIR-SAME:  outputs = "output"
# MLIR:         %[[shape:.*]] = arith.constant dense<[1, -1, 31]> : tensor<3xi32>
# MLIR:         %[[bias:.*]] = "tfl.pseudo_qconst"() <{qtype = tensor<186x!quant.uniform<i32:f32:0

  %1 = "quantfork.dcast"(%0) : (tensor<2x3x3x2x!quant.uniform<i8<-127:127>:f32:3, {0.003937007874015748,0.003937007874015748}>>) -> tensor<2x3x3x2xf32>
  %2 = "quantfork.qcast"(%arg0) : (tensor<1x3x4x3xf32>) -> tensor<1x3x4x3x!quant.uniform<i8:f32, 0.0011764706057660721:-43>>

  }, {
  ^bb0(%arg1: tensor<i32>, %arg2: tensor<f32>):
    %8:2 = func.call @tf.WhileRegion_body(%arg1, %arg2) : (tensor<i32>, tensor<f32>) -> (tensor<i32>, tensor<f32>)
    "tf.Yield"(%8#0, %8#1) : (tensor<i32>, tensor<f32>) -> ()
  }) : (tensor<i32>, tensor<f32>) -> (tensor<i32>, tensor<f32>)
  return
}

// -----

func.func private @my_cond(%arg0: tensor<i32>, %arg1: tensor<f32>) -> tensor<i1> {

  %0 = "tf.TensorListFromTensor"(%arg0, %arg1) : (tensor<5xf32>, tensor<0xi32>) -> tensor<!tf_type.variant<tensor<f32>>>
  %1 = "tf.TensorListSetItem"(%0, %arg2, %arg3) : (tensor<!tf_type.variant<tensor<f32>>>, tensor<i32>, tensor<f32>) -> tensor<!tf_type.variant<tensor<f32>>>
  %2 = "tf.TensorListStack"(%1, %arg1) : (tensor<!tf_type.variant<tensor<f32>>>, tensor<0xi32>) -> tensor<5xf32>

// CHECK-LABEL: func private @composite_dequantize_uniform_
// CHECK-DAG: %[[SCALE:.*]] = "tf.Const"() <{value = dense<0.00866141729> : tensor<f32>}> : () -> tensor<f32>

// CHECK-LABEL: func private @composite_dequantize_uniform
// CHECK-DAG: %[[SCALE:.*]] = "tf.Const"() <{value = dense<0.00866141729> : tensor<f32>}> : () -> tensor<f32>

        "tf.Yield"(%8, %9) : (tensor<i32>, tensor<f32>) -> ()
      }) { is_stateless = false, _xla_outside_compilation = "cluster1"} : (tensor<i32>, tensor<f32>) -> (tensor<i32>, tensor<f32>)
      %c = "tf.C"(%w2) {_xla_outside_compilation = "cluster1"} : (tensor<f32>) -> (tensor<f32>)
      "tf.D"(%c) : (tensor<f32>) -> ()
      tf_device.return

    %6 = stablehlo.clamp %0, %5, %1 : (tensor<f32>, tensor<?x2xf32>, tensor<f32>) -> tensor<?x2xf32>
    return %6 : tensor<?x2xf32>
  }
  func.func private @composite_dot_general_with_bias_and_relu6_dynamic_fn_1(%arg0: tensor<?x2xf32>, %arg1: tensor<2x2xf32>, %arg2: tensor<2xf32>) -> tensor<?x2xf32> attributes {_from_xla_call_module, tf_quant.composite_function} {
    %0 = stablehlo.constant dense<0.000000e+00> : tensor<f32>