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:

// CHECK-NEXT:    return [[RES]], [[MATRES]] : tensor<1x1xf32>, tensor<1x1xf32>
//
module {
  func.func @serving_default(%arg0: tensor<1x3xf32>) -> (tensor<1x1xf32>, tensor<1x1xf32>) {
    %0 = "tf.VarHandleOp"() <{container = "", shared_name = "y"}> : () -> tensor<!tf_type.resource<tensor<3x1xf32>>>
    %2 = "tf.ReadVariableOp"(%0) : (tensor<!tf_type.resource<tensor<3x1xf32>>>) -> tensor<3x1xf32>

  for (Node* n : graph_->nodes()) {
    // We rely on the immutability of Tensors to safely clone Const operations.
    // However, "in place" ops do not respect the immutability of Tensors so we
    // avoid this transformation when such ops are present in the graph.
    //
    // In-place operations are problematic because they break the semantic
    // illusion that tensorflow::Tensor instances are immutable.  For instance

#include "tensorflow/cc/framework/scope.h"
#include "tensorflow/core/framework/tensor.h"

namespace tensorflow {

/// Returns in 'max_error' the maximum element-wise error for dy/dx between the
/// computed and numeric Jacobian matrices where 'xs' and 'ys' are tensors.
/// X_T and Y_T are the c++ types for the x and y tensors, and JAC_T is a
/// real-valued type to store the Jacobian derivatives dy/dx.

    %cst = arith.constant dense<0.000000e+00> : tensor<5xf32>
    %cst_0 = arith.constant dense<1.0> : tensor<5x384xf32>
    %cst_1 = arith.constant dense<1.0> : tensor<5x384xf32>
    %0 = "tfl.fully_connected"(%arg0, %cst_0, %cst) {fused_activation_function = "NONE", keep_num_dims = false, weights_format = "DEFAULT"} : (tensor<?x384xf32>, tensor<5x384xf32>, tensor<5xf32>) -> tensor<?x5xf32>

// CHECK-DAG: %[[input_18:.*]] = "quantfork.stats"({{.*}}) <{layerStats = dense<[-8.000000e-01, 1.600000e+00]> : tensor<2xf32>}> : (tensor<1x4xf32>) -> tensor<1x4xf32>
// CHECK-DAG: %[[input_19:.*]] = "quantfork.stats"({{.*}}) <{layerStats = dense<[-2.000000e+00, 4.000000e+00]> : tensor<2xf32>}> : (tensor<1x2xf32>) -> tensor<1x2xf32>

/*static*/ XlaTensor* XlaTensor::FromTensor(const Tensor* tensor) {
  if (tensor->NumElements() == 0) {
    return nullptr;
  }
  XlaTensor* xla_tensor =
      FromOpaquePointer(const_cast<char*>(tensor->tensor_data().data()));
  return xla_tensor;
}

/*static*/ se::DeviceMemoryBase XlaTensor::DeviceMemoryFromTensor(
    const Tensor& tensor) {
  const XlaTensor* xla_tensor = FromTensor(&tensor);
  if (xla_tensor) {

    sess: Session instance used to evaluate tf.Tensors.

  Returns:
    The new representative dataset where each tf.Tensor is replaced by its
    evaluated numpy ndarrays.
  """
  new_repr_ds = []
  for sample in repr_ds:
    new_sample = {}
    for input_key, input_data in sample.items():
      # Evaluate the Tensor to get the actual value.
      if isinstance(input_data, core.Tensor):

//
// Conditions for `tfl.fully_connected` conversion:
//   * Input tensors are per-tensor uniform quantized (i8->f32)
//     tensors.
//   * The filter tensor is constant a per-tensor uniform quantized (i8->f32)
//     tensor. The quantization dimension should be 1 (the non-contracting
//     dimension).
//   * Output tensors are per-tensor uniform quantized (i8->f32) or
//     per-channel uniform quantized (i32->f32) tensors.

  let summary = "Set a tensor in a promise";

  let description = [{
    Set a tensor in a promise.

    $promise: A value of type !mlrt.promise. The underlying value will always be a tensorflow tensor.
    $tensor: A tensorflow tensor.
  }];

  let arguments = (ins
    MlrtPromiseType:$promise,
    TF_Tensor:$tensor
  );
}

def TFMapFnOp : TensorflowMlrt_Op<"tf_map_fn", [AttrSizedOperandSegments, Pure]> {