const std::vector<int>& tensors = lstm_property.restrict_scale[0];
    if (tensors.size() != 2) {
      op.emitError(
          "Unexpected restricted_scale from operator property."
          " Should only have a pair of indices.");
      return failure();
    }
    return processRestrictScale(op, tensors[0], tensors[1], rewriter);
  }

 private:

// some input tensors are not on that device.
// LINT.IfChange
// Note: Keep in sync with internal copy of enum in eager/context.h.
typedef enum TFE_ContextDevicePlacementPolicy {
  // Running operations with input tensors on the wrong device will fail.
  TFE_DEVICE_PLACEMENT_EXPLICIT = 0,
  // Copy the tensor to the right device but log a warning.
  TFE_DEVICE_PLACEMENT_WARN = 1,

    %tl_set_item = "tf.TensorListSetItem"(%tl, %idx, %elem_1) : (tensor<!tf_type.variant<tensor<*xf32>>>, tensor<i32>, tensor<8xf32>) -> tensor<!tf_type.variant<tensor<*xf32>>>
    %elem_shape_2 = "tf.Const"() {value = dense<-1> : tensor<1xi32>} : () -> tensor<1xi32>

                                  llvm::cl::init("-"));
// NOLINTNEXTLINE
opt<bool> use_splatted_constant(
    "use-splatted-constant",
    llvm::cl::desc(
        "Replace constants with randomly generated splatted tensors"),
    llvm::cl::init(false), llvm::cl::Hidden);
// NOLINTNEXTLINE
opt<bool> input_mlir(
    "input-mlir",
    llvm::cl::desc("Take input TensorFlow model in textual MLIR instead of "

  if (VLOG_IS_ON(1) && status->status.ok()) {
    tensorflow::Tensor tensor;
    if (tensorflow::TF_TensorToTensor(ret, &tensor).ok()) {
      VLOG(1) << "Dequeued tensor content: " << tensor.DebugString();
    }
  }
  return ret;
}

void TF_EnqueueNamedTensor(TF_Session* session, int tensor_id,
                           TF_Tensor* tensor, TF_Status* status) {
  assert(session);
  {

  // FB-NEXT:       } ],

  // CHECK-LABEL: func @main(%arg0: tensor<40x37xf32>, %arg1: tensor<40x37xf32>)
  // CHECK-SAME:      -> tensor<40x40xf32>
  // CHECK:         %[[stat:.*]] = "quantfork.stats"(%arg0) <{layerStats = dense
  // CHECK-SAME:      <[-1.000000e+00, 1.000000e+00]> : tensor<2xf32>}>
  // CHECK-SAME:      : (tensor<40x37xf32>) -> tensor<40x37xf32>
  // CHECK-NEXT:    %[[cst:.*]] = "tfl.pseudo_const"() <{value = dense<

        ) from ex


def _convert_values_to_tf_tensors(
    sample: rd.RepresentativeSample,
) -> Mapping[str, core.Tensor]:
  """Converts TensorLike values of `sample` to Tensors.

  Creates a copy of `sample`, where each value is converted to Tensors
  unless it is already a Tensor.
  The values are not converted in-place (i.e. `sample` is not mutated).

  Args:

    for (const auto& iter : specs.inputs) {
      TensorId tensor_id = ParseTensorName(iter.first);
      feed_fetch_nodes.insert(std::string(tensor_id.node()));
    }
    for (const auto& output : llvm::concat<const std::string>(
             specs.outputs, specs.control_outputs)) {
      TensorId tensor_id = ParseTensorName(output);
      feed_fetch_nodes.insert(std::string(tensor_id.node()));
    }

    std::vector<int> tensors;  // The tensors that are used (input or output) by
                               // this operation. They needn't correspond to
                               // tensors in the TF graph -- we may add fake
                               // tensors to model memory consumed in addition
                               // to input and output tensors. This vector is

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