running models or processes in the same system. GPUs can also have
implementation bugs that might allow attackers to leave malicious code running
and leak or tamper with applications from other users. Please report
vulnerabilities to the vendor of the affected hardware accelerator.

## Reporting vulnerabilities

### Vulnerabilities in TensorFlow

This document covers different use cases for TensorFlow together with comments

limitations under the License.
==============================================================================*/
#ifndef TENSORFLOW_C_EAGER_IMMEDIATE_EXECUTION_TENSOR_HANDLE_H_
#define TENSORFLOW_C_EAGER_IMMEDIATE_EXECUTION_TENSOR_HANDLE_H_

#include "tensorflow/c/eager/abstract_tensor_handle.h"
#include "tensorflow/c/tensor_interface.h"
#include "tensorflow/core/framework/types.pb.h"
#include "tensorflow/core/platform/status.h"

namespace tensorflow {

#ifndef TENSORFLOW_C_EXPERIMENTAL_NEXT_PLUGGABLE_DEVICE_TENSOR_PJRT_BUFFER_UTIL_H_
#define TENSORFLOW_C_EXPERIMENTAL_NEXT_PLUGGABLE_DEVICE_TENSOR_PJRT_BUFFER_UTIL_H_

#include "xla/pjrt/c/pjrt_c_api.h"
#include "xla/pjrt/pjrt_c_api_client.h"
#include "tensorflow/core/framework/tensor.h"

namespace tensorflow {

StatusOr<PJRT_Buffer*> GetPjRtCBufferFromTensor(const Tensor* tensor);

    TFE_TensorHandle* h, TF_Status* status) {
  tensorflow::TensorHandle* handle =
      TensorHandleFromInterface(tensorflow::unwrap(h));
  const tensorflow::Tensor* tensor;
  status->status = handle->Tensor(&tensor);
  if (!status->status.ok()) {
    return nullptr;
  }

  std::vector<int64_t> dev_dims = TensorShapeAsVector(*handle, &status->status);
  if (!status->status.ok()) {
    return nullptr;
  }

  std::vector<tensorflow::string> tensor_id_data;
};

struct TF_ImportGraphDefResults {
  std::vector<TF_Output> return_tensors;
  std::vector<TF_Operation*> return_nodes;
  std::vector<const char*> missing_unused_key_names;
  std::vector<int> missing_unused_key_indexes;

  // Backing memory for missing_unused_key_names values.
  std::vector<tensorflow::string> missing_unused_key_names_data;

    TF_DeleteBuffer(op_list_buffer);
  }
}

void TestEncodeDecode(int line, const std::vector<string>& data) {
  const int64_t n = data.size();
  Status status;
  for (const std::vector<int64_t>& dims :
       std::vector<std::vector<int64_t>>{{n}, {1, n}, {n, 1}, {n / 2, 2}}) {
    // Create C++ Tensor
    Tensor src(tensorflow::DT_STRING, TensorShape(dims));
    for (int64_t i = 0; i < src.NumElements(); ++i) {

  TFE_TensorHandle* input_eager =
      TestScalarTensorHandle<T, datatype>(eager_ctx, value);
  *tensor =
      unwrap(TF_CreateAbstractTensorFromEagerTensor(input_eager, status.get()));
  return absl::OkStatus();
}

// Places data from `t` into *result_tensor.
Status GetValue(AbstractTensorHandle* t, TF_Tensor** result_tensor);
}  // namespace tensorflow

    TF_Status* out_status) const {
  Status status;
  if (reader_ != nullptr) {
    status = reader_->GetTensor(name, out_tensor);
  } else {
    tensorflow::DataType dtype;
    tensorflow::TensorShape shape;
    status = v2_reader_->LookupDtypeAndShape(name, &dtype, &shape);
    if (status.ok()) {
      out_tensor->reset(new Tensor(dtype, shape));

  TFE_DeleteContextOptions(opts);

  std::vector<int64_t> dims(4, 1);
  TFE_Op* op = TFE_NewOp(ctx, "AvgPool", status);
  ASSERT_EQ(TF_OK, TF_GetCode(status)) << TF_Message(status);

  TF_Tensor* tensor =
      TF_AllocateTensor(TF_FLOAT, dims.data(), dims.size(), sizeof(float));
  float tensor_data[] = {1};
  memcpy(TF_TensorData(tensor), tensor_data, TF_TensorByteSize(tensor));

      ctx, inputs[0], inputs[1], &loss, &backprop,
      "SparseSoftmaxCrossEntropyWithLogits"));
  // `gradient_checker` only works with model that returns only 1 tensor.
  // Although, `ops::SparseSoftmaxCrossEntropyWithLogits` returns 2 tensors, the
  // second tensor isn't needed for computing gradient so we could safely drop
  // it.
  outputs[0] = loss;
  backprop->Unref();
  return absl::OkStatus();
}