    def __init__(
        self,
        input_feat,
        hidden_units,
        dropout1,
        dropout2,
        activation_fn="swish",
        bias=True,
    ):
        """
        Args:
            input_feat: Input feature dimension
            hidden_units: Hidden unit dimension
            dropout1: dropout value for layer1
            dropout2: dropout value for layer2
            activation_fn: Name of activation function
            bias: If linear layers should have bias
        """

        super(FeedForwardModule, self).__init__()
        self.layer_norm = LayerNorm(input_feat)
        self.w_1 = torch.nn.Linear(input_feat, hidden_units, bias=bias)
        self.w_2 = torch.nn.Linear(hidden_units, input_feat, bias=bias)
        self.dropout1 = torch.nn.Dropout(dropout1)
        self.dropout2 = torch.nn.Dropout(dropout2)
        self.activation = get_activation_fn(activation_fn)(hidden_units)
Args:
input_feat: Input feature dimension
hidden_units: Hidden unit dimension
dropout1: dropout value for layer1
dropout2: dropout value for layer2
activation_fn: Name of activation function
bias: If linear layers should have bias
fairseq/modules/conformer_layer.py