    def __init__(
        self,
        input_size: int,
        output_size: int = 256,
        attention_heads: int = 4,
        linear_units: int = 2048,
        num_blocks: int = 6,
        dropout_rate: float = 0.1,
        positional_dropout_rate: float = 0.1,
        attention_dropout_rate: float = 0.0,
        input_layer: str = "conv2d",
        pos_enc_layer_type: str = "abs_pos",
        normalize_before: bool = True,
        static_chunk_size: int = 0,
        use_dynamic_chunk: bool = False,
        global_cmvn: torch.nn.Module = None,
        use_dynamic_left_chunk: bool = False,
    ):
        """
        Args:
            input_size (int): input dim
            output_size (int): dimension of attention
            attention_heads (int): the number of heads of multi head attention
            linear_units (int): the hidden units number of position-wise feed
                forward
            num_blocks (int): the number of decoder blocks
            dropout_rate (float): dropout rate
            attention_dropout_rate (float): dropout rate in attention
            positional_dropout_rate (float): dropout rate after adding
                positional encoding
            input_layer (str): input layer type.
                optional [linear, conv2d, conv2d6, conv2d8]
            pos_enc_layer_type (str): Encoder positional encoding layer type.
                opitonal [abs_pos, scaled_abs_pos, rel_pos, no_pos]
            normalize_before (bool):
                True: use layer_norm before each sub-block of a layer.
                False: use layer_norm after each sub-block of a layer.
            static_chunk_size (int): chunk size for static chunk training and
                decoding
            use_dynamic_chunk (bool): whether use dynamic chunk size for
                training or not, You can only use fixed chunk(chunk_size > 0)
                or dyanmic chunk size(use_dynamic_chunk = True)
            global_cmvn (Optional[torch.nn.Module]): Optional GlobalCMVN module
            use_dynamic_left_chunk (bool): whether use dynamic left chunk in
                dynamic chunk training
        """
        super().__init__()
        self._output_size = output_size

        if pos_enc_layer_type == "abs_pos":
            pos_enc_class = PositionalEncoding
        elif pos_enc_layer_type == "rel_pos":
            pos_enc_class = RelPositionalEncoding
        elif pos_enc_layer_type == "no_pos":
            pos_enc_class = NoPositionalEncoding
        else:
            raise ValueError("unknown pos_enc_layer: " + pos_enc_layer_type)

        if input_layer == "linear":
            subsampling_class = LinearNoSubsampling
        elif input_layer == "conv2d":
            subsampling_class = Conv2dSubsampling4
        elif input_layer == "conv2d6":
            subsampling_class = Conv2dSubsampling6
        elif input_layer == "conv2d8":
            subsampling_class = Conv2dSubsampling8
        else:
            raise ValueError("unknown input_layer: " + input_layer)

        self.global_cmvn = global_cmvn
        self.embed = subsampling_class(
            input_size,
            output_size,
            dropout_rate,
            pos_enc_class(output_size, positional_dropout_rate),
        )

        self.normalize_before = normalize_before
        self.after_norm = torch.nn.LayerNorm(output_size, eps=1e-5)
        self.static_chunk_size = static_chunk_size
        self.use_dynamic_chunk = use_dynamic_chunk
        self.use_dynamic_left_chunk = use_dynamic_left_chunk
Args:
input_size (int): input dim
output_size (int): dimension of attention
attention_heads (int): the number of heads of multi head attention
linear_units (int): the hidden units number of position-wise feed
forward
num_blocks (int): the number of decoder blocks
dropout_rate (float): dropout rate
attention_dropout_rate (float): dropout rate in attention
positional_dropout_rate (float): dropout rate after adding
positional encoding
input_layer (str): input layer type.
optional [linear, conv2d, conv2d6, conv2d8]
pos_enc_layer_type (str): Encoder positional encoding layer type.
opitonal [abs_pos, scaled_abs_pos, rel_pos, no_pos]
normalize_before (bool):
True: use layer_norm before each sub-block of a layer.
False: use layer_norm after each sub-block of a layer.
static_chunk_size (int): chunk size for static chunk training and
decoding
use_dynamic_chunk (bool): whether use dynamic chunk size for
training or not, You can only use fixed chunk(chunk_size > 0)
or dyanmic chunk size(use_dynamic_chunk = True)
global_cmvn (Optional[torch.nn.Module]): Optional GlobalCMVN module
use_dynamic_left_chunk (bool): whether use dynamic left chunk in
dynamic chunk training
modules/wenet_extractor/transformer/encoder.py