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Solve the following problem using Python, implementing the functions described below, one line at a time
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Description:
 def d_acquisition_function(self, x):
 """ Returns the gradient of the acquisition function at x. """

        x = np.atleast_2d(x)

        if self.transform=='softplus':
            fval = -self.acq.acquisition_function(x)[:,0]
            scale = 1./(np.log1p(np.exp(fval))*(1.+np.exp(-fval)))
        elif self.transform=='none':
            fval = -self.acq.acquisition_function(x)[:,0]
            scale = 1./fval
        else:
            scale = 1.

        if self.X_batch is None:
            _, grad_acq_x = self.acq.acquisition_function_withGradients(x)
            return scale*grad_acq_x
        else:
            _, grad_acq_x = self.acq.acquisition_function_withGradients(x)
            return scale*grad_acq_x  - self._d_hammer_function(x, self.X_batch, self.r_x0, self.s_x0)