<SYSTEM_TASK:>
Solve the following problem using Python, implementing the functions described below, one line at a time
<END_TASK>
<USER_TASK:>
Description:
 def draw(self, x, y, xlabel=None, ylabel=None):
 """ Draw the joint plot for the data in x and y. Parameters x, y : 1D array-like The data to plot for the x axis and the y axis xlabel, ylabel : str The labels for the x and y axes. """

        # This is a little weird to be here, but it is the best place to perform
        # this computation given how fit calls draw and returns.
        self.corr_ = self.correlation_methods[self.correlation](x, y)

        # First draw the joint plot
        joint_kws = self.joint_kws or {}
        joint_kws.setdefault("alpha", self.alpha)
        joint_kws.setdefault("label", "{}={:0.3f}".format(self.correlation, self.corr_))

        # Draw scatter joint plot
        if self.kind == "scatter":
            self.ax.scatter(x, y, **joint_kws)

            # TODO: Draw best fit line (or should this be kind='reg'?)

        # Draw hexbin joint plot
        elif self.kind in ('hex', 'hexbin'):
            joint_kws.setdefault("mincnt", 1)
            joint_kws.setdefault("gridsize", 50)
            self.ax.hexbin(x, y, **joint_kws)

        # Something bad happened
        else:
            raise ValueError("unknown joint plot kind '{}'".format(self.kind))

        # Set the X and Y axis labels on the plot
        self.ax.set_xlabel(xlabel)
        self.ax.set_ylabel(ylabel)

        # If we're not going to draw histograms, stop here
        if not self.hist:
            # Ensure the current axes is always the main joint plot axes
            plt.sca(self.ax)
            return self.ax

        # Draw the histograms
        hist_kws = self.hist_kws or {}
        hist_kws.setdefault("bins", 50)
        if self.hist == "density":
            hist_kws.setdefault("density", True)

        self.xhax.hist(x, **hist_kws)
        self.yhax.hist(y, orientation="horizontal", **hist_kws)

        # Ensure the current axes is always the main joint plot axes
        plt.sca(self.ax)
        return self.ax