    def __init__(
        self,
        idim: int,
        hidden_units: int,
        dropout_rate: float,
        activation: torch.nn.Module = torch.nn.ReLU(),
        adaptive_scale: bool = False,
        init_weights: bool = False,
    ):
        """Construct a PositionwiseFeedForward object."""
        super(PositionwiseFeedForward, self).__init__()
        self.idim = idim
        self.hidden_units = hidden_units
        self.w_1 = torch.nn.Linear(idim, hidden_units)
        self.activation = activation
        self.dropout = torch.nn.Dropout(dropout_rate)
        self.w_2 = torch.nn.Linear(hidden_units, idim)
        self.ada_scale = None
        self.ada_bias = None
        self.adaptive_scale = adaptive_scale
        self.ada_scale = torch.nn.Parameter(
            torch.ones([1, 1, idim]), requires_grad=adaptive_scale
        )
        self.ada_bias = torch.nn.Parameter(
            torch.zeros([1, 1, idim]), requires_grad=adaptive_scale
        )
        if init_weights:
            self.init_weights()
Construct a PositionwiseFeedForward object.
modules/wenet_extractor/squeezeformer/positionwise_feed_forward.py