// off, and an execution operation for named operations. Often execution will
// simply wrap op execution on one or more physical devices.
//
// device_info is an opaque caller-defined type stored with the custom device
// which is passed to the functions referenced in the TFE_CustomDevice struct
// `device` (execute, delete_device, etc.). It can for example contain the
// names of wrapped devices.
//

              TFE_TensorHandle* axis);

// If there is a device of type `device_type`, returns true
// and sets 'device_name' accordingly.
// `device_type` must be either "GPU" or "TPU".
bool GetDeviceName(TFE_Context* ctx, tensorflow::string* device_name,
                   const char* device_type);

// Create a ServerDef with the given `job_name` and add `num_tasks` tasks in it.

  // Length of the list if is_list is true. Undefined otherwise.
  int64_t list_size;

  // Type of elements of the list if is_list != 0.
  // Type of the single value stored in the attribute if is_list == 0.
  TF_AttrType type;

  // Total size the attribute value.
  // The units of total_size depend on is_list and type.
  // (1) If type == TF_ATTR_STRING and is_list == 0
  //     then total_size is the byte size of the string

  // least one input of the op is watched and has trainable dtype.
  //
  // op_type is used to decide which of the incoming gradients can be left as
  // nullptr instead of building zeros when build_default_zeros_grads == true.
  void RecordOperation(
      const string& op_type, const std::vector<TapeTensor>& output_tensors,
      absl::Span<const int64_t> input_tensor_id,

// A create function. This will never actually get called in this test, it's
// just nice to know that it compiles.
void* create(TF_OpKernelConstruction* ctx) {
  TF_DataType type;
  TF_Status* s = TF_NewStatus();
  TF_OpKernelConstruction_GetAttrType(ctx, "foobar", &type, s);
  TF_DeleteStatus(s);
  return NULL;
}

// A compute function. This will never actually get called in this test, it's
// just nice to know that it compiles.

TF_CAPI_EXPORT extern void TF_CreateAndSetPjRtCApiClient(
    const char* device_type, TF_Status* status, PJRT_NamedValue* create_options,
    int num_options);

// Resets the PjRt client for a device. After this, `TF_GetPjRtCClient` will
// returns an error for that device.
TF_CAPI_EXPORT extern void TF_ResetPjRtCClient(const char* device_type,
                                               TF_Status* status);

                                    Tensor* tensor);

absl::StatusOr<xla::PjRtCApiClient*> GetPjRtCApiClient(
    const DeviceType& device_type);

absl::Status ResetPjRtClient(const DeviceType& device_type);

}  // namespace tensorflow

                 std::unique_ptr<tensorflow::Tensor>* out_tensor,
                 TF_Status* out_status) const;

 private:
  // Uses "v2_reader_" to build "var name -> shape" and "var name -> data type"
  // maps; both owned by caller.
  // REQUIRES: "v2_reader_ != nullptr && v2_reader_.status().ok()".
  std::pair<std::unique_ptr<TensorSliceReader::VarToShapeMap>,

                absl::Span<AbstractTensorHandle*> outputs, bool use_function);

Status BuildImmediateExecutionContext(bool use_tfrt, AbstractContext** ctx);

// Return a tensor handle with given type, values and dimensions.
template <class T, TF_DataType datatype>
Status TestTensorHandleWithDims(AbstractContext* ctx, const T* data,
                                const int64_t* dims, int num_dims,