keras/src/layers/rnn/conv_lstm.py/ConvLSTMCell/bias_initializer
class ConvLSTMCell:    def build(self, inputs_shape, states_shape=None):
        if self.data_format == "channels_first":
            channel_axis = 1
            self.spatial_dims = inputs_shape[2:]
        else:
            channel_axis = -1
            self.spatial_dims = inputs_shape[1:-1]
        if None in self.spatial_dims:
            raise ValueError(
                "ConvLSTM layers only support static "
                "input shapes for the spatial dimension. "
                f"Received invalid input shape: input_shape={inputs_shape}"
            )
        if inputs_shape[channel_axis] is None:
            raise ValueError(
                "The channel dimension of the inputs (last axis) should be "
                "defined. Found None. Full input shape received: "
                f"input_shape={inputs_shape}"
            )
        self.input_spec = InputSpec(
            ndim=self.rank + 3, shape=(None,) + inputs_shape[1:]
        )

        input_dim = inputs_shape[channel_axis]
        self.input_dim = input_dim
        self.kernel_shape = self.kernel_size + (input_dim, self.filters * 4)
        recurrent_kernel_shape = self.kernel_size + (
            self.filters,
            self.filters * 4,
        )

        self.kernel = self.add_weight(
            shape=self.kernel_shape,
            initializer=self.kernel_initializer,
            name="kernel",
            regularizer=self.kernel_regularizer,
            constraint=self.kernel_constraint,
        )
        self.recurrent_kernel = self.add_weight(
            shape=recurrent_kernel_shape,
            initializer=self.recurrent_initializer,
            name="recurrent_kernel",
            regularizer=self.recurrent_regularizer,
            constraint=self.recurrent_constraint,
        )

        if self.use_bias:
            if self.unit_forget_bias:

                def bias_initializer(_, *args, **kwargs):
                    return ops.concatenate(
                        [
                            self.bias_initializer(
                                (self.filters,), *args, **kwargs
                            ),
                            initializers.get("ones")(
                                (self.filters,), *args, **kwargs
                            ),
                            self.bias_initializer(
                                (self.filters * 2,), *args, **kwargs
                            ),
                        ]
                    )

            else:
                bias_initializer = self.bias_initializer
            self.bias = self.add_weight(
                shape=(self.filters * 4,),
                name="bias",
                initializer=bias_initializer,
                regularizer=self.bias_regularizer,
                constraint=self.bias_constraint,
            )
        else:
            self.bias = None
        self.built = True