class ImmediateExecutionTensorHandle : public AbstractTensorHandle {
 public:
  // Returns number of dimensions.
  virtual Status NumDims(int* num_dims) const = 0;
  // Returns number of elements across all dimensions.
  virtual Status NumElements(int64_t* num_elements) const = 0;
  // Returns size of specified dimension
  //
  // -1 indicates an unknown axis length; this is unreachable for most standard

  TF_EXPECT_OK(ReadCache(&cache, "", 2 * block_size, 1, &out));
  // Element at block_size was evicted.  Reading this element will also cause
  // the LRU element (at 0) to be evicted.
  calls.push_back(block_size);
  TF_EXPECT_OK(ReadCache(&cache, "", block_size, 1, &out));
  // Element at 0 was evicted again.
  calls.push_back(0);
  TF_EXPECT_OK(ReadCache(&cache, "", 0, 1, &out));
}

  std::unique_ptr<NowSecondsEnv> env(new NowSecondsEnv);
  tf_gcs_filesystem::ExpiringLRUCache<int> cache(
      1, 0, [&env]() { return env->NowSeconds(); });
  env->SetNowSeconds(1);
  // Verify that replacement of an existing element works, and updates the
  // timestamp of the entry.
  cache.Insert(key, 41);
  env->SetNowSeconds(2);
  cache.Insert(key, 42);
  // 1 second after the most recent insertion, the entry is still valid.

  const int num_dims = 2;
  int64_t* dims = new int64_t[num_dims];
  int64_t num_elements = 1;
  dims[0] = batch_size;
  dims[1] = 1;
  for (int64_t i = 0; i < num_dims; ++i) {
    num_elements *= dims[i];
  }
  TF_Tensor* t = TF_AllocateTensor(TF_STRING, dims, num_dims,
                                   sizeof(TF_TString) * num_elements);
  delete[] dims;

                          std::vector<int64_t> shape,
                          std::vector<int64_t> strides) {
  size_t num_elements = 1;
  for (int i = 0; i < static_cast<int32_t>(shape.size()); ++i) {
    num_elements *= shape[i];
  }
  std::vector<float> data(num_elements);
  for (size_t j = 0; j < num_elements; ++j) {
    data[j] = j;
  }
  DLManagedTensor dlm_in = {};
  DLTensor* dltensor_in = &dlm_in.dl_tensor;

// The return value was current at the time of TFE_TensorDebugInfo creation.
TF_CAPI_EXPORT extern int TFE_TensorDebugInfoOnDeviceNumDims(
    TFE_TensorDebugInfo* debug_info);

// Returns the number of elements in dimension `dim_index`.
// Tensor representation on device can be transposed from its representation
// on host. The data contained in dimension `dim_index` on device

  shapes.resize(num_shapes);
  for (int i = 0; i < num_shapes; ++i) {
    if (proto_lens[i] > std::numeric_limits<int>::max()) {
      status->status = InvalidArgument(
          "length of element ", i, " in the list (", proto_lens[i],
          " bytes) is too large to be parsed by the protocol buffer library");
      return;
    }
    if (!shapes[i].ParseFromArray(protos[i], static_cast<int>(proto_lens[i]))) {

// `oper` and `num_dims` with the corresponding number of dimensions. On return,
// for every i where `num_dims[i]` > 0, `dims[i]` will be an array of
// `num_dims[i]` elements. A value of -1 for `num_dims[i]` indicates that the
// i-th shape in the list is unknown.
//
// The elements of `dims` will point to addresses in `storage` which must be
// large enough to hold at least `storage_size` int64_ts.  Ideally, `num_shapes`

  std::vector<int32_t> data(1, 0);
  xla::Shape shape = xla::ShapeUtil::MakeShape(xla::S32, {1});

  auto buffer = c_api_client->pjrt_c_client()->client->BufferFromHostBuffer(
      data.data(), shape.element_type(), shape.dimensions(),
      /*byte_strides=*/std::nullopt,
      xla::PjRtClient::HostBufferSemantics::kImmutableOnlyDuringCall, nullptr,
      c_api_client->pjrt_c_client()->client->addressable_devices()[0]);

  // Convert from AbstractTensor to TF_Tensor.
  TF_Tensor* theta_tensor;
  TF_RETURN_IF_ERROR(GetValue(theta, &theta_tensor));

  // Get number of elements and fill data.
  int num_elems = TF_TensorElementCount(theta_tensor);
  vector<float> theta_data(num_elems);
  memcpy(theta_data.data(), TF_TensorData(theta_tensor),
         TF_TensorByteSize(theta_tensor));