// needs to be statically registered in some tests and utilities for building
// the API files at the time of creating the pip package. Hence, we need to
// expose this function so that this filesystem can be statically registered
// when needed.
void TF_InitPlugin(TF_FilesystemPluginInfo* info);

  // Sessions are added to this map in TF_NewSession, and removed in
  // TF_DeleteSession.
  // TF_Graph may only / must be deleted when
  //   sessions.size() == 0 && delete_requested == true
  //
  // TODO(b/74949947): mutations currently trigger a warning instead of a bad
  // status, this should be reverted when possible.
  tensorflow::gtl::FlatMap<TF_Session*, tensorflow::string> sessions
      TF_GUARDED_BY(mu);

// APIs for generically dealing with op attributes (e.g. when forwarding them
// through custom device implementations).
//
// TODO(allenl): Currently these are black boxes, but we should have some way to
// inspect values. This would let people e.g. copy over most attributes and then
// modify some based on their values.

// A reference to an op's name -> attribute mapping

void TF_DeleteAbstractTensor(TF_AbstractTensor*);

// TF_OutputList holds the list of TF_AbstractTensor that results from executing
// an operation, or provided to create a function.
// When executing an operation in an eager context, the expected number of
// outputs must be set beforehand with `TF_OutputListSetNumOutputs`.
typedef struct TF_OutputList TF_OutputList;
TF_OutputList* TF_NewOutputList();

  // Returns size of specified dimension
  //
  // -1 indicates an unknown axis length; this is unreachable for most standard
  // ImmediateExecutionTensorHandles, but comes up for example when computing
  // the shape of a parallel tensor with component shapes differing across
  // devices.
  virtual Status Dim(int dim_index, int64_t* dim) const = 0;

  // Returns the device which created the handle.

// Run a function containing a MatMul op and check its output.
// If heavy_load_on_streaming_rpc is true, send some rpc requests before the one
// which creates a remote input, to simulate a scenario that the remote input
// is not ready when we start running an op or a function.
void TestRemoteExecuteSilentCopies(bool async, bool remote, bool func,
                                   bool heavy_load_on_streaming_rpc,

// nodes. This may cause incorrectness in graph analyses, but is useful for
// simulation or scheduling.
// If aggressive_shape_inference is true, nodes are executed on the host to
// identify output values when possible and does other aggressive strategies.
// This may cause incorrectness in graph analyses, but is useful for simulation
// or scheduling.
// If include_input_tensor_values is true, the values of constant

/// `const`.
///
/// For consistency, the arguments on all these functions follow the same
/// pattern: first we have the opaque pointer argument ("this" above), then the
/// input arguments, then the in-out arguments (if any) and we finish the
/// argument list with the out arguments. We only use the return type for an out
/// parameter if that is a plain C type, as this ensures ABI compatibility

/// This class should be shared by read-only random access files on a remote
/// filesystem (e.g. GCS).
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

  // a trainable dtype.
  bool ShouldRecord(
      absl::Span<const AbstractTensorHandle* const> tensors) const;
  // Unwatches this tensor on the tape. Mainly used for cleanup when deleting
  // eager tensors.
  void DeleteTrace(const AbstractTensorHandle*);

  // Consumes the internal state of the tape (so cannot be called more than