def __init__(
        self,
        embed_dim,
        ffn_embed_dim,
        attention_heads,
        dropout,
        use_fp16,
        depthwise_conv_kernel_size=31,
        activation_fn="swish",
        attn_type=None,
        pos_enc_type="abs",
    ):
        """
        Args:
            embed_dim: Input embedding dimension
            ffn_embed_dim: FFN layer dimension
            attention_heads: Number of attention heads in MHA
            dropout: dropout value
            depthwise_conv_kernel_size: Size of kernel in depthwise conv layer in convolution module
            activation_fn: Activation function name to use in convulation block and feed forward block
            attn_type: MHA implementation from ESPNET vs fairseq
            pos_enc_type: Positional encoding type - abs, rope, rel_pos
        """
        self.pos_enc_type = pos_enc_type
        super(ConformerEncoderLayer, self).__init__()

        self.ffn1 = FeedForwardModule(
            embed_dim,
            ffn_embed_dim,
            dropout,
            dropout,
        )

        self.self_attn_layer_norm = LayerNorm(embed_dim, export=False)
        self.self_attn_dropout = torch.nn.Dropout(dropout)
        if attn_type == "espnet":
            if self.pos_enc_type == "rel_pos":
                self.self_attn = RelPositionMultiHeadedAttention(
                    embed_dim,
                    attention_heads,
                    dropout=dropout,
                )
            elif self.pos_enc_type == "rope":
                self.self_attn = RotaryPositionMultiHeadedAttention(
                    embed_dim, attention_heads, dropout=dropout, precision=use_fp16
                )
            elif self.pos_enc_type == "abs":
                self.self_attn = ESPNETMultiHeadedAttention(
                    embed_dim,
                    attention_heads,
                    dropout=dropout,
                )
            else:
                raise Exception(f"Unsupported attention type {self.pos_enc_type}")
        else:
            # Default to fairseq MHA
            self.self_attn = MultiheadAttention(
                embed_dim,
                attention_heads,
                dropout=dropout,
            )

        self.conv_module = ConvolutionModule(
            embed_dim=embed_dim,
            channels=embed_dim,
            depthwise_kernel_size=depthwise_conv_kernel_size,
            dropout=dropout,
            activation_fn=activation_fn,
        )

        self.ffn2 = FeedForwardModule(
            embed_dim,
            ffn_embed_dim,
            dropout,
            dropout,
            activation_fn=activation_fn,
        )
        self.final_layer_norm = LayerNorm(embed_dim, export=False)

        Args:
            embed_dim: Input embedding dimension
            ffn_embed_dim: FFN layer dimension
            attention_heads: Number of attention heads in MHA
            dropout: dropout value
            depthwise_conv_kernel_size: Size of kernel in depthwise conv layer in convolution module
            activation_fn: Activation function name to use in convulation block and feed forward block
            attn_type: MHA implementation from ESPNET vs fairseq
            pos_enc_type: Positional encoding type - abs, rope, rel_pos
        
understand_icl/fairseq/fairseq/modules/conformer_layer.py