TF_Buffer** properties, int num_values, TF_Status* status);

// Helper to maintain a map between function names in a given
// FunctionDefLibrary and function definitions.
// Typical use case, is to look up an OpDef by type name.
typedef struct TF_FunctionLibraryDefinition TF_FunctionLibraryDefinition;

// Create NewFunctionLibraryDefinition.
TF_CAPI_EXPORT extern TF_FunctionLibraryDefinition*

// Update an existing context with a new set of servers defined in a ServerDef
// proto. Servers can be added to and removed from the list of remote workers
// in the context. A New set of servers identified by the ServerDef must be up
// when the context is updated.
//
// This API is for experimental usage and may be subject to change.
TF_CAPI_EXPORT extern void TFE_ContextUpdateServerDef(TFE_Context* ctx,

TFE_Op* IdentityOp(TFE_Context* ctx, TFE_TensorHandle* a);

// Return a shape op fetching the shape of `a`.
TFE_Op* ShapeOp(TFE_Context* ctx, TFE_TensorHandle* a);

// Return an allreduce op adding up input tensor `in` from `group_size` workers.
TFE_Op* AllReduceOp(TFE_Context* ctx, TFE_TensorHandle* in, int group_size);

// Return a SendOp op `op_name` with send input tensor `in` and attributes

  // Builds a tensor filled with ones with the same shape and dtype as `t`.
  virtual Status BuildOnesLike(const TapeTensor& t,
                               Gradient** result) const = 0;

  // Looks up the ID of a Gradient.
  virtual int64_t TensorId(Gradient* tensor) const = 0;

  // Converts a Gradient to a TapeTensor.
  virtual TapeTensor TapeTensorFromGradient(Gradient* gradient) const = 0;

class ImmediateExecutionContext;
class ServerDef;
class WorkerEnv;
class WorkerCacheInterface;

class ImmediateExecutionDistributedManager {
 public:
  virtual ~ImmediateExecutionDistributedManager() {}

  // Set up distributed execution environment on local and remote tasks.
  // When `reset_context` is true, initialize new cluster context state based
  // on cluster configurations provided in `server_def`; otherwise, update

    // Target operations
    const TF_Operation* const* target_opers, int ntargets,
    // RunMetadata
    TF_Buffer* run_metadata,
    // Output status
    TF_Status*);

// Set up the graph with the intended feeds (inputs) and fetches (outputs) for a
// sequence of partial run calls.
//
// On success, returns a handle that is used for subsequent PRun calls. The

#if !defined(IS_MOBILE_PLATFORM)
  // Set up a multi-client distributed execution environment. Must be called on
  // all tasks in the cluster.
  // This call internally coordinates with other tasks to initialize the eager
  // context and TF server for multi-client execution.
  virtual Status EnableCollectiveOps(const ServerDef& server_def) = 0;

  // Set a distributed manager that helps set up, update, and check liveness

// `ctx`. The `tensorflow::PluginResource` wraps a resource by plugin (as a
// opaque pointer, since TensorFlow cannot parse it). `delete_func` is needed
// for ResourceMgr to clean up the resource. `status` will be set.
TF_CAPI_EXPORT extern void TF_CreatePluginResource(
    TF_OpKernelContext* ctx, const char* container_name,
    const char* plugin_resource_name, void* plugin_resource,

// workers name). Operations created in this context can then be executed on
// any of these remote workers by setting an appropriate device.
//
// If the following is set, all servers identified by the
// ServerDef must be up when the context is created.
TF_CAPI_EXPORT extern void TFE_ContextSetServerDef(TFE_Context* ctx,
                                                   int keep_alive_secs,

/// The `TF_InitPlugin` function is used by plugins to set up the data
/// structures that implement this interface, as presented in Section 2. In
/// order to not have plugin shared objects call back symbols defined in core
/// TensorFlow, `TF_InitPlugin` has a `TF_FilesystemPluginInfo` argument which
/// the plugin must fill (using the `TF_SetFilesystemVersionMetadata` for the
/// metadata and setting up all the supported operations and the URI schemes