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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 predict(self, data):
 """Predict samples on actual data. The result of this function is used for calculating the residuals. Parameters data : array, shape (trials, channels, samples) or (channels, samples) Epoched or continuous data set. Returns ------- predicted : array, shape `data`.shape Data as predicted by the VAR model. Notes ----- Residuals are obtained by r = x - var.predict(x) """

        data = atleast_3d(data)
        t, m, l = data.shape

        p = int(np.shape(self.coef)[1] / m)

        y = np.zeros(data.shape)
        if t > l - p:  # which takes less loop iterations
            for k in range(1, p + 1):
                bp = self.coef[:, (k - 1)::p]
                for n in range(p, l):
                    y[:, :, n] += np.dot(data[:, :, n - k], bp.T)
        else:
            for k in range(1, p + 1):
                bp = self.coef[:, (k - 1)::p]
                for s in range(t):
                    y[s, :, p:] += np.dot(bp, data[s, :, (p - k):(l - k)])

        return y