#include <sys/stat.h>

namespace tf_posix_filesystem {

// Transfers up to `size` bytes from `dst_fd` to `src_fd`.
//
// This method uses `sendfile` if available (i.e., linux 2.6.33 or later) or an
// intermediate buffer if not.
//
// Returns number of bytes transferred or -1 on failure.
int CopyFileContents(int dst_fd, int src_fd, off_t size);

  // it in this context.
  virtual AbstractOperation* CreateOperation() = 0;

  // Registers a function with this context, after this the function is
  // available to be called/referenced by its name in this context.
  virtual Status RegisterFunction(AbstractFunction*) = 0;
  // Remove a function. 'func' argument is the name of a previously added

                                                           TF_Tensor** result,
                                                           TF_Status* status);

// TODO(josh11b): Register OpDef, available to all operations added
// to this graph.

// --------------------------------------------------------------------------
// API for driving Graph execution.

typedef struct TF_Session TF_Session;

// Destroy an options object.
TF_CAPI_EXPORT extern void TFE_DeleteContextOptions(TFE_ContextOptions*);

// "Context" under which operations/functions are executed. It encapsulates
// things like the available devices, resource manager etc.
// TFE_Context must outlive all tensor handles created using it. In other
// words, TFE_DeleteContext() must be called after all tensor handles have
// been deleted (with TFE_DeleteTensorHandle).

// any gradients to be computed).
//
// Finally, we start a backprop stack with a set of tape entries for which we
// have all gradients available. This set usually is a subset of the set of
// targets (not all since targets which have outputs in the tape will not have
// gradients available initially).
//
// Then we repeatedly pop an entry from the stack, run its backprop, and update

void TF_DeleteAbstractFunction(TF_AbstractFunction*);

// Register the function with the given context. This is particularly useful for
// making a function available to an eager context.
void TF_ExecutionContextRegisterFunction(TF_ExecutionContext*,
                                         TF_AbstractFunction*, TF_Status*);

                         absl::Span<AbstractTensorHandle*> grad_inputs) = 0;
  virtual ~GradientFunction() {}
};

// Metadata from the forward operation that is made available to the
// gradient registerer to instantiate a GradientFunction.
struct ForwardOperation {
 public:
  string op_name;
  std::vector<AbstractTensorHandle*> inputs;
  std::vector<AbstractTensorHandle*> outputs;

  // Runtime. This is necessary to decouple runtime-dependent
  // code that is layered on top of the runtime.
  virtual bool UsesTFRT() = 0;

  // List attributes of available devices
  virtual void ListDevices(std::vector<DeviceAttributes>* devices) = 0;

  // Add `devices` into context's device manager. Context's device manager
  // will take ownership and maintain devices' lifetime.

  /// Reads up to `n` bytes from `*file` starting at `offset`.
  ///
  /// The output is in `buffer`, core TensorFlow owns the buffer and guarantees
  /// that at least `n` bytes are available.
  ///
  /// Returns number of bytes read or -1 in case of error. Because of this
  /// constraint and the fact that `ssize_t` is not defined in `stdint.h`/C++
  /// standard, the return type is `int64_t`.
  ///