def predict(self, inputs: Array, bandwidth: float = 0.1):
        """
        Predict the target

        Parameters
        ----------
        inputs: Array
            Inputs.
        bandwidth: float
            Kernel bandwidth.

        Returns
        -------
        Predictions for the given inputs.
        """
        """"""
        if inputs.ndim > 1:
            raise ValueError("`inputs` must be a one-dimensional array.")
        inputs = jnp.copy(inputs)
        kernels = vmap(
            lambda x: self.evaluate_scaled_kernel(
                x - self.train_inputs, bandwidth=bandwidth
            )
        )(inputs)
        m = jnp.sum(kernels * self.standardized_train_targets[None], axis=1) / jnp.sum(
            kernels, axis=1
        )
        m *= self._std_train_targets
        m += self._mean_train_targets
        return m

        Predict the target

        Parameters
        ----------
        inputs: Array
            Inputs.
        bandwidth: float
            Kernel bandwidth.

        Returns
        -------
        Predictions for the given inputs.
        
fortuna/kernel_regression/nadaraya_watson.py