CHECK(SerializeToTString(s, output_tstring));
  TF_DeleteTensor(summary_tensor);
}

bool IsSameSize(TF_Tensor* tensor1, TF_Tensor* tensor2) {
  if (TF_NumDims(tensor1) != TF_NumDims(tensor2)) {
    return false;
  }
  for (int d = 0; d < TF_NumDims(tensor1); d++) {
    if (TF_Dim(tensor1, d) != TF_Dim(tensor2, d)) {
      return false;
    }
  }
  return true;
}

std::string SingleTag(TF_Tensor* tags) {

                                s_);
  ASSERT_EQ(TF_OK, TF_GetCode(s_)) << TF_Message(s_);

  const char* tensor_data[] = {&tensor1[0], &tensor2[0]};
  const size_t tensor_size[] = {TF_ARRAYSIZE(tensor1), TF_ARRAYSIZE(tensor2)};
  const int64_t* tensor_dims[] = {&dims1[0], &dims2[0]};
  const size_t tensor_ndims[] = {ndims1, ndims2};
  for (int i = 0; i < 2; ++i) {
    TF_Tensor* v = values[i];

package com.acme;

import dagger.Module;
import dagger.Provides;

@Module
public class Sensors {
    @Provides static HeatSensor provideHeater() {
        return new DefaultHeatSensor();
    }

  // Create a DeviceMemoryBase from a Tensor. The Tensor can be an XlaTensor, in
  // which case the returned value is shaped_buffer()->root_buffer(), or a
  // normal Tensor in which case the returned value is
  // {tensor.tensor_data().data(), tensor.tensor_data().size}.
  static se::DeviceMemoryBase DeviceMemoryFromTensor(const Tensor& tensor);

  // Assign the internal ShapedBuffer to new memory for the given dtype and

  %0 = "tf.TensorListFromTensor"(%arg0, %arg1) : (tensor<*xf32>, tensor<1xi32>) -> tensor<!tf_type.variant<tensor<*xf32>>>
  %1 = "tf.TensorListGetItem"(%0, %arg2, %arg1) : (tensor<!tf_type.variant<tensor<*xf32>>>, tensor<i32>, tensor<1xi32>) -> tensor<*xf32>
  %2 = "tf.TensorListStack"(%0, %arg1) : (tensor<!tf_type.variant<tensor<*xf32>>>, tensor<1xi32>) -> tensor<*xf32>
  func.return %1, %2 : tensor<*xf32>, tensor<*xf32>

// of intermediate tensors since importer doesn't guarantee to preserve tensor
// names except output tensors.
Location OpLoc(const OperatorT& op,
               const std::vector<std::unique_ptr<tflite::TensorT>>& tensors,
               Builder builder, Location base) {
  if (op.outputs.empty()) return base;

  llvm::SmallVector<Location, 4> locations;
  locations.reserve(op.outputs.size());

package com.acme;

import dagger.Module;
import dagger.Provides;

@Module
public class Sensors {
    @Provides static HeatSensor provideHeater() {
        return new DefaultHeatSensor();
    }

  DataType data_type() const { return data_type_; }
  Status status() const { return status_; }
  const Tensor& tensor() const { return tensor_; }

 private:
  Status status_;
  Output output_ = Output(Operation(nullptr), 0);
  Tensor tensor_;
  const std::string node_name_ = "";
  int32 index_ = 0;
  DataType data_type_ = DT_INVALID;
};

// Tensor types
def TF_BoolTensor : TensorOf<[TF_Bool]>;

def TF_IntTensor : TensorOf<[TF_Int]>;
def TF_Int4Tensor : TensorOf<[TF_Int4]>;
def TF_Int8Tensor : TensorOf<[TF_Int8]>;
def TF_Int16Tensor : TensorOf<[TF_Int16]>;
def TF_Int32Tensor : TensorOf<[TF_Int32]>;
def TF_Int64Tensor : TensorOf<[TF_Int64]>;
def TF_I32OrI64Tensor : TensorOf<[TF_I32OrI64]>;

    auto* ret = emptyTensor->tensor;
    delete emptyTensor;
    return ret;
  }

  Tensor tensor;
  if (!tensor.CopyFrom(src, src.shape())) {
    return nullptr;
  }
  return new tensorflow::TensorInterface(std::move(tensor));
}

// Non-static for testing.
TF_Tensor* TF_TensorFromTensor(const tensorflow::Tensor& src, Status* status) {