TFE_TensorHandle* tensor,
                                                       TF_Status* status) const;

  // Construct a parallel tensor consisting of the scalar values from `values`.
  template <typename DataType>
  std::unique_ptr<ParallelTensor> ScalarsFromSequence(
      absl::Span<const DataType> values, TFE_Context* context,

namespace tensorflow {
namespace parallel_device {

// A helper for performing common operations on variables. A much more
// restricted stand-in for tf.Variable in Python.
class Variable {
 public:
  // Construct a Variable from a resource-dtype TFE_TensorHandle and an
  // indication of the dtype of the variable's value.
  //
  // Note that creating this resource-dtype handle can fail, so `Create` is a

TF_CAPI_EXPORT extern void TF_FunctionToFunctionDef(TF_Function* func,
                                                    TF_Buffer* output_func_def,
                                                    TF_Status* status);

// Construct and return the function whose FunctionDef representation is
// serialized in `proto`. `proto_len` must equal the number of bytes
// pointed to by `proto`.
// Returns:

                        // of block_size.
  std::unique_ptr<ExpiringLRUCache<GcsFileStat>> stat_cache;
  GCSFile(google::cloud::storage::Client&& gcs_client);
  // This constructor is used for testing purpose only.
  GCSFile(google::cloud::storage::Client&& gcs_client, bool compose,
          uint64_t block_size, size_t max_bytes, uint64_t max_staleness,

// Sets a thread-local device placement policy. After this call, other calls to
// TFE_Execute in the same thread will use the device policy specified here
// instead of the device policy used to construct the context. This has no
// effect on the device policy used by other program threads.
TF_CAPI_EXPORT extern void TFE_ContextSetThreadLocalDevicePlacementPolicy(
    TFE_Context* ctx, TFE_ContextDevicePlacementPolicy policy);

                                               void* device_info);

  // Method to execute an operation.
  //
  // Arguments provide enough information to reconstruct the original `TFE_Op`,
  // or construct a transformed version, by inspecting the passed `op`.
  //
  // TFE_OpGetDevice(op) records the original placement of the operation. It may

TF_CAPI_EXPORT extern void TF_DeleteShapeAndTypeListArray(
    TF_ShapeAndTypeList** shape_list_array, int num_items);

// Infer shapes for the given `op`. The arguments mimic the arguments of the
// `shape_inference::InferenceContext` constructor. Note the following:
//   - The inputs of the `op` are not used for shape inference. So, it is
//     OK to not have the inputs properly set in `op`. See `input_tensors`

// context. The provided `ctx` is consumed by this API call and deleted.
// The returned TF_AbstractFunction must be deleted by the client,
// TODO(aminim): clarify the contract on the state of the context after this
// call.
TF_AbstractFunction* TF_FinalizeFunction(TF_ExecutionContext* ctx,
                                         TF_OutputList*, TF_Status*);

#if !defined(IS_MOBILE_PLATFORM) && !defined(IS_SLIM_BUILD)
#include "tensorflow/core/framework/op_gen_lib.h"
#endif  // !defined(IS_MOBILE_PLATFORM) && !defined(IS_SLIM_BUILD)
#include "tensorflow/core/common_runtime/graph_constructor.h"
#include "tensorflow/core/common_runtime/shape_refiner.h"
#include "tensorflow/core/framework/function.h"
#include "tensorflow/core/framework/tensor.h"
#include "tensorflow/core/framework/tensor_shape.h"

class RamFileBlockCache {
 public:
  /// The callback executed when a block is not found in the cache, and needs to
  /// be fetched from the backing filesystem. This callback is provided when the
  /// cache is constructed. It returns total bytes read ( -1 in case of errors
  /// ). The `status` should be `TF_OK` as long as the read from the remote
  /// filesystem succeeded (similar to the semantics of the read(2) system
  /// call).