keras/src/layers/convolutional/conv1d.py/Conv1D/call
class Conv1D:    def call(self, inputs):
        padding = self.padding
        if self.padding == "causal":
            # Apply causal padding to inputs.
            inputs = ops.pad(inputs, self._compute_causal_padding())
            padding = "valid"

        outputs = ops.conv(
            inputs,
            self.kernel,
            strides=list(self.strides),
            padding=padding,
            dilation_rate=self.dilation_rate,
            data_format=self.data_format,
        )

        if self.use_bias:
            if self.data_format == "channels_last":
                bias_shape = (1,) * (self.rank + 1) + (self.filters,)
            else:
                bias_shape = (1, self.filters) + (1,) * self.rank
            bias = ops.reshape(self.bias, bias_shape)
            outputs += bias

        if self.activation is not None:
            return self.activation(outputs)
        return outputs