} : (tensor<1x2x3x4x!quant.uniform<i8:f32, 1.000000e+00:3>>, tensor<1x2x4x5x!quant.uniform<i8:f32, 2.000000e+00>>) -> tensor<1x2x3x5x!quant.uniform<i32:f32, 4.000000e+00:1>>
  %1 = stablehlo.uniform_quantize %0 : (tensor<1x2x3x5x!quant.uniform<i32:f32, 4.000000e+00:1>>) -> tensor<1x2x3x5x!quant.uniform<i8:f32, 6.000000e+00:5>>
  return %1 : tensor<1x2x3x5x!quant.uniform<i8:f32, 6.000000e+00:5>>
}

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

// CHECK: return %[[es]] : tensor<1x2x3x4x!tf_type.string>
}

// CHECK-LABEL: decompose_tf_consecutive
func.func @decompose_tf_consecutive(%arg0: tensor<1x2x3x4x!tf_type.string>, %arg1: tensor<f32>, %arg2: tensor<f32>) -> tensor<f32> {
  %0 = "tfr.cast"(%arg0) : (tensor<1x2x3x4x!tf_type.string>) -> !tfr.tensor

// CHECK: %[[cst:.*]] = arith.constant dense<[-1, 3]> : tensor<2xi32>
// CHECK-NEXT: %[[q1:.*]] = "tfl.quantize"(%arg0) <{qtype = tensor<1x1x3x!quant.uniform<i16:f32, {{.*}}>>}> {volatile} : (tensor<1x1x3xf32>) -> tensor<1x1x3x!quant.uniform<i16:f32, {{.*}}>>
// CHECK-NEXT: %[[dq1:.*]] = "tfl.dequantize"(%[[q1]]) : (tensor<1x1x3x!quant.uniform<i16:f32, {{.*}}>>) -> tensor<1x1x3xf32>

  %0 = "tfl.strided_slice"(%arg0, %arg1, %arg2, %arg3) {begin_mask = 0 : i32, ellipsis_mask = 0 : i32, end_mask = 0 : i32, new_axis_mask = 0 : i32, shrink_axis_mask = 0 : i32, offset = false} : (tensor<12x2x2x5xui8>, tensor<1xi32>, tensor<1xi32>, tensor<1xi32>) -> tensor<1x2x2x5x!tf_type.quint8>
  func.return %0 : tensor<1x2x2x5x!tf_type.quint8>
}

func.func @decompose_tf_no_op(%arg0: tensor<1x2x3x4x!tf_type.string>) -> tensor<1x2x3x4x!tf_type.string> {
  %0 = "tf.FakeNoOp"(%arg0) : (tensor<1x2x3x4x!tf_type.string>) -> tensor<1x2x3x4x!tf_type.string>
  func.return %0 : tensor<1x2x3x4x!tf_type.string>

// CHECK-NEXT: return %arg0
}

// CHECK-LABEL: decompose_tf_intermediate
func.func @decompose_tf_intermediate(%arg0: tensor<1x2x3x4x!tf_type.string>) -> tensor<1x2x3x4x!tf_type.string> {

  %0 = stablehlo.convolution(%arg0, %arg1) dim_numbers = [b, f, 0, 1]x[o, i, 0, 1]->[b, f, 0, 1], window = {pad = [[1, 1], [1, 1]]} {batch_group_count = 1 : i64, feature_group_count = 1 : i64} : (tensor<1x4x3x3x!quant.uniform<i8:f32, 1.000000e+0:-100>>, tensor<2x4x3x3x!quant.uniform<i8:f32:0, {2.000000e+2, 3.000000e+3}>>) -> tensor<1x2x3x3x!quant.uniform<i8:f32, 4.000000e+0>>

  %0 = "tfl.quantize"(%arg0) {qtype = tensor<1x3x3x!quant.uniform<i8:f32, 0.003:-128>>} : (tensor<1x3x3xf32>) -> tensor<1x3x3x!quant.uniform<i8:f32, 0.003:-128>>
  %1 = "tfl.logistic"(%0) : (tensor<1x3x3x!quant.uniform<i8:f32, 0.003:-128>>) -> tensor<1x3x3x!quant.uniform<i8:f32, 3.906250e-03:-128>>

    // CHECK: "tf.XlaCallModule"(%[[dq_act]], %[[dq_weight]]
    %1 = "tf.XlaCallModule"(%0, %cst_0, %cst) {
      Sout = [#tf_type.shape<1x2x2x2>], config = "",
      module = "composite_conv2d_with_bias_and_relu6_fn_10",
      _entry_function = @composite_conv2d_with_bias_and_relu6_fn_10,
      // Represents a per-channel quantization for the operand index 1 with

func.func @NotQuantizeReadVariable() -> tensor<1x2x3x!quant.uniform<u8<1:255>:f32, 0.047244094488188976:128>> {
  %0 = "tfl.var_handle"() {container = "", shared_name = "states"} : () -> tensor<!tf_type.resource<tensor<1x2x3xf32>>>
  %1 = "tfl.read_variable"(%0) : (tensor<!tf_type.resource<tensor<1x2x3xf32>>>) -> tensor<1x2x3xf32>