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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 spmt(t, peak_delay=6, under_delay=16, peak_disp=1, under_disp=1, p_u_ratio=6):
 """Normalized SPM HRF function from sum of two gamma PDFs Parameters t : array-like vector of times at which to sample HRF Returns ------- hrf : array vector length ``len(t)`` of samples from HRF at times `t` Notes ----- [1] This is the canonical HRF function as used in SPM. It has the following defaults: - delay of response (relative to onset) : 6s - delay of undershoot (relative to onset) : 16s - dispersion of response : 1s - dispersion of undershoot : 1s - ratio of response to undershoot : 6s - onset : 0s - length of kernel : 32s References: ----- [1] http://nipy.org/ [2] https://github.com/fabianp/hrf_estimation """

    return spm_hrf_compat(t, peak_delay=peak_delay, under_delay=under_delay,
                          peak_disp=peak_disp, under_disp=under_disp,
                          p_u_ratio=p_u_ratio, normalize=True)