}

  // Read all underlying tensors of the variables from the session.
  std::vector<Tensor> tensors;
  tensors.reserve(resource_tensors.size());
  for (const Tensor& resource_tensor : resource_tensors) {
    if (resource_tensor.dtype() != tensorflow::DT_RESOURCE) {
      tensors.push_back(resource_tensor);
      continue;
    }

    for (const int itensor : operations_[ioperation].tensors) {
      if (const auto& tensor = tensors_[itensor];
          tensor.first_use() >= begin && tensor.first_use() < end &&
          tensor.last_use() > peak_loc) {
        for (const int ioperation : tensor.operations) {
          // We return the earliest dependence on any output tensor.
          if (ioperation > peak_loc && ioperation < best) {

// in the plugin for Training. The API takes in the context with indices for the
// input and value tensors. It also accepts the copy callback provided by
// pluggable vendor to do the copying of the tensors and the update callback to
// apply the arithmetic operation. The caller takes ownership of the `source`,
// `dest`, `tensor` and `value` tensors and is responsible for freeing them with
// TF_DeleteTensor.

      });
}
bool Tape::ShouldRecord(
    absl::Span<const AbstractTensorHandle* const> tensors) const {
  std::vector<int64_t> tensor_ids(tensors.size());
  std::vector<tensorflow::DataType> tensor_dtypes(tensors.size());
  for (int i = 0; i < tensors.size(); i++) {
    tensor_ids[i] = ToId(tensors[i]);
    tensor_dtypes[i] = tensors[i]->DataType();
  }
  return GradientTape::ShouldRecord(tensor_ids, tensor_dtypes);
}

TF_CAPI_EXPORT extern TF_Tensor* TF_TensorMaybeMove(TF_Tensor* tensor);

// Destroy a tensor.
TF_CAPI_EXPORT extern void TF_DeleteTensor(TF_Tensor*);

// Return the type of a tensor element.
TF_CAPI_EXPORT extern TF_DataType TF_TensorType(const TF_Tensor*);

// Set a new shape for the Tensor.
TF_CAPI_EXPORT extern void TF_SetShape(TF_Tensor* tensor, const int64_t* dims,

  Status ToTensor(tensorflow::Tensor* dst) const;
  Status BitcastFrom(const TensorInterface& from, DataType type,
                     const int64_t* new_dims, int num_new_dims);
  Status FromProto(const tensorflow::TensorProto& from);

  tensorflow::Tensor& Tensor() { return tensor_; }

 private:
  tensorflow::Tensor tensor_;
};

inline Tensor& TensorFromInterface(AbstractTensorInterface* tensor) {

    [Pure, CoreRT_TypedAttributeTrait,
     TFRT_CostFunctionInterface, TFRT_FixedCost<1>]> {
  let summary = "Create a tensorflow tensor from serialized tensor proto";

  let description = [{
    Create a tensorflow tensor from serilaized tensor proto.

    Example:
      %tensor = tfrt_fallback_async.const_tensor_proto "<serialized proto content>"
  }];

  let arguments = (ins
    StrAttr:$serialized_tensor_proto

func.func @sub(%arg0: tensor<3x2xf32>) -> tensor<3x2xf32> attributes {tf.entry_function = {inputs = "serving_default_x", outputs = "outputs"}} {
  %0 = "tfl.pseudo_const" () {value = dense<[[1.0, 2.0], [3.0, 4.0], [5.0, 6.0]]> : tensor<3x2xf32>} : () -> tensor<3x2xf32>
  %1 = "tfl.sub" (%0, %arg0) {fused_activation_function = "NONE"} : (tensor<3x2xf32>, tensor<3x2xf32>) -> tensor<3x2xf32>
  func.return %1 : tensor<3x2xf32>
}
}

// CHECK:      {

// CHECK-NEXT: }

func.func @main(%arg0: tensor<1xf32>, %arg1: tensor<1xf32>) -> tensor<1xf32> {
  %0 = "tfl.less"(%arg0, %arg1) : (tensor<1xf32>, tensor<1xf32>) -> tensor<1xi1>
  %1 = "tf.If"(%0, %arg0, %arg1) {else_branch = @cond_false, then_branch = @cond_true, is_stateless = false} : (tensor<1xi1>, tensor<1xf32>, tensor<1xf32>) -> tensor<1xf32>
  func.return %1 : tensor<1xf32>
}

                                       TFE_TensorHandle* tensor,
                                       TF_Status* status, void* device_info) {
  TF_SetStatus(
      status, TF_UNIMPLEMENTED,
      absl::StrCat("Trying to copy a tensor ",
                   tensorflow::unwrap(tensor)->DebugString(),
                   " on to a parallel device. Pack non-parallel "
                   "tensors for each device into a parallel tensor explicitly.")