000002f0  80 28 db ec 9d f9 f4 9a  06 12 8a 32 89 11 4a e5  |.(.........2..J.|
00000300  b6 68 98 21 ec f1 75 9c  f9 28 bc b1 99 5b ec 77  |.h.!..u..(...[.w|
00000310  37 8b 6b 05 ad b3 10 de  24 05 84 a4 16 fe 1d 84  |7.k.....$.......|
00000320  af 5d 28 7d d7 bc de 2f  81 da a3 79 fb c0 a5 43  |.](}.../...y...C|
00000330  b6 3b 8d 90 30 00 80 98  16 83 ac 50 43 c7 3e 76  |.;..0......PC.>v|
00000340  c5 dc 85 c2 19 75 a6 90  f8 2f 71 40 d9 a0 73 7e  |.....u.../q@..s~|

  %9 = "tfl.no_value"() {value} : () -> none
  %10 = "tfl.pseudo_const"() {value = dense<0.000000e+00> : tensor<3xf32>} : () -> tensor<3xf32>
  %11 = "tfl.pseudo_const"() {value = dense<1.000000e+00> : tensor<3xf32>} : () -> tensor<3xf32>
  %recurrent_input = "tfl.pseudo_const"() {value = dense<0.000000e+00> : tensor<1x3xf32>} : () -> tensor<1x3xf32>

  func.func @depthwise_conv(%arg0: tensor<1x3x4x3xf32>) -> (tensor<*xf32>, tensor<*xf32>) {
    %cst_0 = "tf.Const"() {value = dense<0.000000e+00> : tensor<3xf32>} : () -> tensor<3xf32>
    %cst_1 = "tf.Const"() {value = dense<3.000000e+00> : tensor<2x3x3x1xf32>} : () -> tensor<2x3x3x1xf32>
    %cst_2 = "tf.Const"() {value = dense<3.000000e+00> : tensor<2x3x3x2xf32>} : () -> tensor<2x3x3x2xf32>

  // CHECK: %[[VAL_0:.*]] = "tf._FusedConv2D"(%arg2, %arg1, %arg0) <{data_format = "NHWC", dilations = [1, 1, 1, 1], epsilon = 0.000000e+00 : f32, explicit_paddings = [], fused_ops = ["BiasAdd"], num_args = 1 : i64, operandSegmentSizes = array<i32: 1, 1, 1, 0>, padding = "SAME", strides = [1, 1, 1, 1], use_cudnn_on_gpu = true}> {TArgs = [f32]} : (tensor<8x32x32x3xf32>, tensor<1x1x3x128xf32>,...

  %4 = "tfl.reshape"(%3, %cst) : (tensor<1x112x112x32x!quant.uniform<i8:f32, 0.023528476789885875>>, tensor<2xi32>) -> tensor<1x401408x!quant.uniform<i8:f32, 0.023528476789885875>>
  %5 = "tfl.softmax"(%4) <{beta = 1.000000e+00 : f32}> : (tensor<1x401408x!quant.uniform<i8:f32, 0.023528476789885875>>) -> tensor<1x401408x!quant.uniform<i8:f32, 3.906250e-03>>

      tf_saved_model.initializer_type = "restore_op",
      tf_saved_model.exported_names = ["tf_saved_model.session_initializer_restore_op"]} {
    %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>>>

00000310  5b 02 17 dc 20 7d 18 19  21 a4 70 f4 4b 99 bf b5  |[... }..!.p.K...|
00000320  cd d7 07 59 a5 82 ed 85  03 06 5c 34 3f c8 c3 4d  |...Y......\4?..M|
00000330  c3 fb 96 05 bf b5 bd bf  e2 28 07 7e 51 a6 84 90  |.........(.~Q...|
00000340  bf 9e 2e f6 b5 04 8e 06  7a 63 c8 00 84 a1 a3 2c  |........zc.....,|
00000350  f3 6f 52 52 c4 ce 4a 59  31 1f d4 ab 2e f4 75 90  |.oRR..JY1.....u.|
00000360  a5 3b ff ab 20 be 51 92  c5 f4 4d 8b f2 2a a7 ff  |.;.. .Q...M..*..|

// CHECK-LABEL: float_conv
func.func @float_conv(%arg0: tensor<1x3x4x3xf32>, %arg1: tensor<2x3x3x2xf32>) -> (tensor<*xf32>, tensor<*xf32>, tensor<*xf32>) {
  %cst = "tf.Const"() {value = dense<0.000000e+00> : tensor<2xf32>} : () -> tensor<2xf32>
  %0 = "tf.Conv2D"(%arg0, %arg1) {
    data_format = "NHWC", device = "", dilations = [1, 1, 1, 1], explicit_paddings = [],
    padding = "SAME", strides = [1, 1, 2, 1], use_cudnn_on_gpu = true

// CHECK-DAG: %[[CST_0:.*]] = "tf.Const"() <{{{.*value = dense<1.000000e\+00> : tensor<8xf32>.*}}}>
// CHECK-DAG: %[[VAR_HANDLE_0:.*]] = "tf.VarHandleOp"() {{.*shared_name = "const_0".*}}
// CHECK-DAG: "tf.AssignVariableOp"(%[[VAR_HANDLE_0]], %[[CST_0]])

// CHECK-DAG: %[[CST_1:.*]] = "tf.Const"() <{{{.*value = dense<2.000000e\+00> : tensor<8xf32>.*}}}>

    %0 = stablehlo.constant dense<0.000000e+00> : tensor<f32>
    %1 = stablehlo.constant dense<6.000000e+00> : tensor<f32>
    %2 = stablehlo.dot_general %arg0, %arg1, contracting_dims = [1] x [0], precision = [DEFAULT, DEFAULT] : (tensor<?x2xf32>, tensor<2x2xf32>) -> tensor<?x2xf32>