int version = TFE_CUSTOM_DEVICE_VERSION;
  // Method to copy a tensor to the custom device.
  TFE_TensorHandle* (*copy_tensor_to_device)(TFE_Context* context,
                                             TFE_TensorHandle* tensor,
                                             TF_Status* status,
                                             void* device_info);

  // Method to copy a tensor from the custom device to a target device.

//
// We expose this here to allow fine-grained synchronization in multi-threaded
// workloads, which is required since the Python implementation depends on the
// above mutation methods. This allows us to prevent modifications to nodes in
// the graph after the session has been made aware of them.
TF_CAPI_EXPORT extern void TF_ExtendSession(TF_Session* session,

/// the names will lose the `Modular` part. Until that point, the `Modular*`
/// classes here are experimental and subject to breaking changes.
/// For documentation on these methods, consult `core/platform/filesystem.h`.

namespace tensorflow {

// TODO(b/143949615): After all filesystems are converted, this file will be
// moved to core/platform, and this class can become a singleton and replace the

        : backward_tape(backward_tape), accumulating(accumulating) {}
    // Set temporarily while in the Accumulate method; if backward_tape is not
    // nullptr then we forward op executions to it so Accumulate can compute a
    // backward pass on its backward function.
    //
    // Not owned by the ForwardAccumulator. The method which sets
    // `backward_tape` keeps ownership.

  // Configure graph collection in RunMetadata.
  virtual void SetShouldStoreGraphs(bool value) = 0;

  // Return the collected RunMetadata. This method will transfer the ownership
  // to the caller.
  virtual std::unique_ptr<RunMetadata> ExportRunMetadata() = 0;

  // For LLVM style RTTI.
  static bool classof(const AbstractContext* ptr) {