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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 acm(x, l):
 """Compute autocovariance matrix at lag l. This function calculates the autocovariance matrix of `x` at lag `l`. Parameters x : array, shape (n_trials, n_channels, n_samples) Signal data (2D or 3D for multiple trials) l : int Lag Returns ------- c : ndarray, shape = [nchannels, n_channels] Autocovariance matrix of `x` at lag `l`. """

    x = atleast_3d(x)

    if l > x.shape[2]-1:
        raise AttributeError("lag exceeds data length")

    ## subtract mean from each trial
    #for t in range(x.shape[2]):
    #    x[:, :, t] -= np.mean(x[:, :, t], axis=0)

    if l == 0:
        a, b = x, x
    else:
        a = x[:, :, l:]
        b = x[:, :, 0:-l]

    c = np.zeros((x.shape[1], x.shape[1]))
    for t in range(x.shape[0]):
        c += a[t, :, :].dot(b[t, :, :].T) / a.shape[2]
    c /= x.shape[0]

    return c.T