    def __init__(
        self,
        embed_dim,
        channels,
        depthwise_kernel_size,
        dropout,
        activation_fn="swish",
        bias=False,
        export=False,
    ):
        """
        Args:
            embed_dim: Embedding dimension
            channels: Number of channels in depthwise conv layers
            depthwise_kernel_size: Depthwise conv layer kernel size
            dropout: dropout value
            activation_fn: Activation function to use after depthwise convolution kernel
            bias: If bias should be added to conv layers
            export: If layernorm should be exported to jit
        """
        super(ConvolutionModule, self).__init__()
        assert (
            depthwise_kernel_size - 1
        ) % 2 == 0, "kernel_size should be a odd number for 'SAME' padding"
        self.layer_norm = LayerNorm(embed_dim, export=export)
        self.pointwise_conv1 = torch.nn.Conv1d(
            embed_dim,
            2 * channels,
            kernel_size=1,
            stride=1,
            padding=0,
            bias=bias,
        )
        self.glu = torch.nn.GLU(dim=1)
        self.depthwise_conv = torch.nn.Conv1d(
            channels,
            channels,
            depthwise_kernel_size,
            stride=1,
            padding=(depthwise_kernel_size - 1) // 2,
            groups=channels,
            bias=bias,
        )
        self.batch_norm = torch.nn.BatchNorm1d(channels)
        self.activation = get_activation_fn(activation_fn)(channels)
        self.pointwise_conv2 = torch.nn.Conv1d(
            channels,
            embed_dim,
            kernel_size=1,
            stride=1,
            padding=0,
            bias=bias,
        )
        self.dropout = torch.nn.Dropout(dropout)
Args:
embed_dim: Embedding dimension
channels: Number of channels in depthwise conv layers
depthwise_kernel_size: Depthwise conv layer kernel size
dropout: dropout value
activation_fn: Activation function to use after depthwise convolution kernel
bias: If bias should be added to conv layers
export: If layernorm should be exported to jit
fairseq/modules/conformer_layer.py