on:
  - pr_created
  - commit
  - comment_added

automations:

  # If someone is a primary author of the files in a change, but NOT the pr author, automatically add them as a reviewer.
  add_usual_expert:
    if:
      - {{ ('add_usual_expert' | isEnabledAutomation(pr)) }}
      - {{ repo | codeExperts(gt=50) | match(branch.author) | nope }}
    run:
      - action: add-reviewers@v1
        args:

    public static final String LABELS_PERMISSIONS = "{labels.permissions}";

    /** The key of the message: Virtual Hosts */
    public static final String LABELS_virtual_hosts = "{labels.virtual_hosts}";

    /** The key of the message: Virtual Host */
    public static final String LABELS_virtual_host = "{labels.virtual_host}";

    /** The key of the message: Label */

    built with Visual Studio 2019 version 16.4 in order to take advantage of the
    new `/d2ReducedOptimizeHugeFunctions` compiler flag. To use these new
    packages, you must install "Microsoft Visual C++ Redistributable for Visual
    Studio 2015, 2017 and 2019", available from Microsoft's website
    [here](https://support.microsoft.com/help/2977003/the-latest-supported-visual-c-downloads).

  virtual Status SetAttrInt(const char* attr_name, int64_t value) = 0;
  virtual Status SetAttrFloat(const char* attr_name, float value) = 0;
  virtual Status SetAttrBool(const char* attr_name, bool value) = 0;
  virtual Status SetAttrType(const char* attr_name, DataType value) = 0;
  virtual Status SetAttrShape(const char* attr_name, const int64_t* dims,
                              const int num_dims) = 0;

  // Optimized scalar creation functions
  virtual AbstractTensorInterface* CreateInt64Scalar(int64_t value) = 0;
  virtual AbstractTensorInterface* CreateUint64Scalar(uint64 value) = 0;
  virtual AbstractTensorInterface* CreateInt32Scalar(int32_t value) = 0;
  virtual AbstractTensorInterface* CreateFloatScalar(float value) = 0;
  virtual AbstractTensorInterface* CreateDoubleScalar(double value) = 0;

  // devices.
  virtual Status Dim(int dim_index, int64_t* dim) const = 0;

  // Returns the device which created the handle.
  virtual const char* DeviceName(Status* status) const = 0;
  // Returns the device where the tensor was placed.
  virtual const char* BackingDeviceName(Status* status) const = 0;
  // Returns the device type which created the handle.
  virtual const char* DeviceType(Status* status) const = 0;

  AbstractOpAttrsKind getKind() const { return kind_; }
  virtual ~AbstractOpAttrs() = default;

  // Returns the AbstractFunction as a FunctionDef.
  virtual void GetNameAttrList(
      tensorflow::NameAttrList* name_and_attrs) const = 0;

  virtual bool GetInt(absl::string_view, int64_t* result) const = 0;
  virtual bool GetFloat(absl::string_view attr_name, float* result) const = 0;

  // that the argument need to be handled by a custom device.
  virtual bool HasCustomDeviceInput() const = 0;

  virtual const tensorflow::OpDef* OpDef() const = 0;

  virtual Status InputLength(const char* input_name, int* length) = 0;
  virtual Status OutputLength(const char* output_name, int* length) = 0;

  // Set stack trace to be used for potential async error reporting.
  virtual void SetStackTrace(ManagedStackTrace stack_trace) = 0;

  // destroy an instance of this class.
  virtual void Release() = 0;

  // Creates an operation builder and ties it to this context.
  // The returned object can be used for setting operation's attributes,
  // adding inputs and finally executing (immediately or lazily as in tracing)
  // it in this context.
  virtual AbstractOperation* CreateOperation() = 0;

In the end, the ultimate objective is to be able to **serve your API clients** in a way that is **secure**, to **avoid disruptions**, and to use the **compute resources** (for example remote servers/virtual machines) as efficiently as possible. 🚀