keras/src/layers/rnn/conv_lstm1d.py/ConvLSTM1D/__init__
class ConvLSTM1D:    def __init__(
        self,
        filters,
        kernel_size,
        strides=1,
        padding="valid",
        data_format=None,
        dilation_rate=1,
        activation="tanh",
        recurrent_activation="sigmoid",
        use_bias=True,
        kernel_initializer="glorot_uniform",
        recurrent_initializer="orthogonal",
        bias_initializer="zeros",
        unit_forget_bias=True,
        kernel_regularizer=None,
        recurrent_regularizer=None,
        bias_regularizer=None,
        activity_regularizer=None,
        kernel_constraint=None,
        recurrent_constraint=None,
        bias_constraint=None,
        dropout=0.0,
        recurrent_dropout=0.0,
        seed=None,
        return_sequences=False,
        return_state=False,
        go_backwards=False,
        stateful=False,
        **kwargs
    ):
        super().__init__(
            rank=1,
            filters=filters,
            kernel_size=kernel_size,
            strides=strides,
            padding=padding,
            data_format=data_format,
            dilation_rate=dilation_rate,
            activation=activation,
            recurrent_activation=recurrent_activation,
            use_bias=use_bias,
            kernel_initializer=kernel_initializer,
            recurrent_initializer=recurrent_initializer,
            bias_initializer=bias_initializer,
            unit_forget_bias=unit_forget_bias,
            kernel_regularizer=kernel_regularizer,
            recurrent_regularizer=recurrent_regularizer,
            bias_regularizer=bias_regularizer,
            activity_regularizer=activity_regularizer,
            kernel_constraint=kernel_constraint,
            recurrent_constraint=recurrent_constraint,
            bias_constraint=bias_constraint,
            return_sequences=return_sequences,
            return_state=return_state,
            go_backwards=go_backwards,
            stateful=stateful,
            dropout=dropout,
            recurrent_dropout=recurrent_dropout,
            seed=seed,
            **kwargs
        )