00000100  53 8f f8 3b ed 2c df b9  08 c2 da 60 b9 23 17 50  |S..;.,.....`.#.P|
00000110  da 0f 24 76 15 21 e6 e9  a8 f5 3e 08 cc 1b ee 92  |..$v.!....>.....|
00000120  2b 01 92 8d f9 4f 5a 3a  53 11 fc 32 52 cc af cd  |+....OZ:S..2R...|
00000130  7b 94 0e 76 10 c2 16 36  2d a4 64 69 1c 05 70 20  |{..v...6-.di..p |
00000140  0d 23 cd 4a 33 c5 c7 db  db 0f f8 b6 42 0c 83 0a  |.#.J3.......B...|
00000150  a1 73 68 fb 87 2c 9d d2  d3 cf d7 3a bb 36 7e 83  |.sh..,.....:.6~.|

  %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>
  %cell_input = "tfl.pseudo_const"() {value = dense<1.000000e+00> : tensor<1x3xf32>} : () -> tensor<1x3xf32>

}

// -----

module {
  func.func @conv2d(%arg0: tensor<1x3x4x512xf32>) -> (tensor<*xf32>) {
    %cst_0 = "tf.Const"() {value = dense<0.000000e+00> : tensor<2xf32>} : () -> tensor<2xf32>
    %cst_1 = "tf.Const"() {value = dense<3.000000e+00> : tensor<2x3x512x2xf32>} : () -> tensor<2x3x512x2xf32>

    %15, %16,
    %recurrent_stats, %cell_stats,
    %none, %20, %21, %22) ({}) {
      cell_clip = 5.000000e+01 : f32,
      effective_hidden_scale_intermediate = tensor<!quant.calibrated<f32<-5.000000e-01:5.000000e-01>>>,
      fused_activation_function = "TANH",
      input_to_cell_intermediate = tensor<!quant.calibrated<f32<-4.000000e+00:4.000000e+00>>>,
      input_to_forget_intermediate = tensor<!quant.calibrated<f32<-1.600000e+01:1.600000e+01>>>,

  // CHECK-DAG: %[[BIAS:.*]] = arith.constant dense<[-5.000000e-01, 5.000000e-01, -5.000000e-01, 5.000000e-01, -5.000000e-01, 5.000000e-01, -5.000000e-01, 5.000000e-01, -5.000000e-01, 5.000000e-01, -5.000000e-01, 5.000000e-01, -5.000000e-01, 5.000000e-01, -5.000000e-01, 5.000000e-01, -5.000000e-01, 5.000000e-01, -5.000000e-01, 5.000000e-01, -5.000000e-01, 5.000000e-01, -5.000000e-01, 5.000000e-01, -5.000000e-01,...

      %outputs_0, %control_1 = tf_executor.island(%control) wraps "tf.Const"() {value = dense<0.000000e+00> : tensor<16xf32>} : () -> tensor<16xf32>
      %outputs_2, %control_3 = tf_executor.island(%control_1) wraps "tf.Const"() {value = dense<0.000000e+00> : tensor<5x5x32x16xf32>} : () -> tensor<5x5x32x16xf32>

  %0 = mhlo.constant dense<0.000000e+00> : tensor<f32>
  %1 = "mhlo.pad"(%arg0, %0) <{edge_padding_high = dense<0> : tensor<3xi64>, edge_padding_low = dense<1> : tensor<3xi64>, interior_padding = dense<0> : tensor<3xi64>}> : (tensor<10x10x10xf32>, tensor<f32>) -> tensor<11x11x11xf32>
  %2 = mhlo.constant dense<0.000000e+00> : tensor<f32>

// CHECK-SAME: effective_hidden_scale_intermediate = tensor<*x!quant.calibrated<f32<-5.000000e-01:5.000000e-01>>>
// CHECK-SAME: input_to_cell_intermediate = tensor<*x!quant.calibrated<f32<-4.000000e+00:4.000000e+00>>>
// CHECK-SAME: input_to_forget_intermediate = tensor<*x!quant.calibrated<f32<-1.600000e+01:1.600000e+01>>>
// CHECK-SAME: input_to_input_intermediate = tensor<*x!quant.calibrated<f32<-3.200000e+01:3.200000e+01>>>

}

// -----

// CHECK-LABEL: lift_float_conv
func.func @lift_float_conv(%arg0: tensor<1x3x4x3xf32>) -> (tensor<*xf32>, tensor<*xf32>) {
  %cst = "tf.Const"() {value = dense<0.000000e+00> : tensor<2xf32>} : () -> tensor<2xf32>
  %cst_1 = "tf.Const"() {value = dense<3.000000e+00> : tensor<2x3x3x2xf32>} : () -> tensor<2x3x3x2xf32>
  %0 = "tf.Conv2D"(%arg0, %cst_1) {
    data_format = "NHWC", device = "", dilations = [1, 1, 1, 1], explicit_paddings = [],

// CHECK-NEXT: %[[Q:.*]] = "tfl.quantize"(%arg0) <{qtype = tensor<1x2x!quant.uniform<u8:f32, 1.000000e+00>>}> : (tensor<1x2xf32>) -> tensor<1x2x!quant.uniform<u8:f32, 1.000000e+00>>
// CHECK-NEXT: %[[CST:.*]] = "tfl.pseudo_qconst"() <{qtype = tensor<1x2x!quant.uniform<u8:f32, 1.000000e+00>>, value = dense<-76> : tensor<1x2xi8>}> : () -> tensor<1x2x!quant.uniform<u8:f32, 1.000000e+00>>