// CHECK: return %[[RES0]] : tensor<256x32x32x16xf32>

{
  "version": 3,
  "operator_codes": [
    {
      "builtin_code": "CONV_2D"
    }
  ],
  "subgraphs": [
    {
      "tensors": [
        {
          "shape": [
            256,
            32,

func.func @main(tensor<4xf32>) -> tensor<4xf32> {
^bb0(%arg0: tensor<4xf32>):
// CHECK: {
// CHECK-NEXT:   version: 3,
// CHECK-NEXT:     operator_codes: [ {
// CHECK-NEXT:       deprecated_builtin_code: 80,
// CHECK-NEXT:       version: 1,
// CHECK-NEXT:       builtin_code: FAKE_QUANT
// CHECK-NEXT:     } ],
// CHECK-NEXT:     subgraphs: [ {
// CHECK-NEXT:       tensors: [ {
// CHECK-NEXT:         shape: [ 4 ],

// CHECK-NEXT: }
func.func @main() -> tensor<!tf_type.variant<tensor<2xi32>>> {

      %1 = "tfl.sub"(%arg2, %arg4) {fused_activation_function = "NONE"} :
        (tensor<*xi32>, tensor<i32>) -> tensor<*xi32>
      %2 = tfl.add %arg3, %arg3 {fused_activation_function = "NONE"} : tensor<*xf32>
      "tfl.yield"(%1, %2, %arg4) : (tensor<*xi32>, tensor<*xf32>, tensor<i32>) -> ()
  }) : (tensor<i32>, tensor<1xf32>, tensor<i32>) -> (tensor<i32>, tensor<1xf32>, tensor<i32>)
  func.return %0#1, %0#2 : tensor<1xf32>, tensor<i32>

type `tensor<?x3x224x224xf32>`.

```bash
tf-to-stablehlo-translate <saved-model-path> --input-arg-shapes=4,3,224,224
```

* To convert
[google-bert/bert-large-uncased](https://huggingface.co/google-bert/bert-large-uncased)
to StableHLO with static input shapes `1x12`, `1x12`, and `1x12` for input
arguments with types `tensor<?x?xi32>, tensor<?x?xi32>, tensor<?x?xi32>`.

```bash

// CHECK-EMPTY:

^bb0(%arg0: tensor<4 x f32>, %arg1: tensor<4 x i8>, %arg2: tensor<4 x f32>, %arg3: tensor<4 x f32>):
  %0 = "tfl.pseudo_const" () {value = dense<0.0> : tensor<4xf32>} : () -> tensor<4xf32> loc("Const")
  %1 = "tfl.svdf"(%arg0, %arg1, %arg2, %arg3, %0) {fused_activation_function = "RELU", rank = 2 : i32} : (tensor<4xf32>, tensor<4xi8>, tensor<4xf32>, tensor<4xf32>, tensor<4xf32>) -> tensor<4xf32>
  func.return %1 : tensor<4xf32>

  func.func @main() -> (tensor<5xf32> {tf_saved_model.index_path = ["start_logits"]}) attributes {tf.entry_function = {control_outputs = "", inputs = "", outputs = "StatefulPartitionedCall:1"}, tf_saved_model.exported_names = ["serving_default"]} {
    %cst = arith.constant dense<0.000000e+00> : tensor<5xf32>
    func.return %cst : tensor<5xf32>
  }

                                       std::vector<Tensor>* x_datas,
                                       std::vector<Tensor>* jacobian_ts) {
  size_t y_num = y_shapes.size();
  size_t x_num = x_shapes.size();
  // x_stride and y_stride are used to calculate the correct jacobian row and
  // column position for a pair of elements at positions r, c within the x and y
  // tensors respectively.

  %0 = "tfl.pseudo_const" () {value = dense<true> : tensor<4xi1>} : () -> tensor<4xi1> loc("Const1")
  %1 = "tfl.pseudo_const" () {value = dense<false> : tensor<4xi1>} : () -> tensor<4xi1> loc("Const2")
  %2 = "tfl.logical_or"(%arg0, %1) : (tensor<4xi1>, tensor<4xi1>) -> tensor<4xi1> loc("logical_or")
  %3 = "tfl.logical_and"(%2, %0) : (tensor<4xi1>, tensor<4xi1>) -> tensor<4xi1> loc("logical_and")
  func.return %3 : tensor<4xi1>

) -> _SignatureDefMap:
  """Validates if the tensor names in signatures are consistent with the graph.

  This function checks if the input and output tensor names in the signatures
  exist if the graph. The output tensor names might change during conversion,
  we try to fix that with `_restore_output_tensor_names`. Besides, if there
  are duplicated tensor names, they we will be prefixed with the signature name.