human
I wrote the below code

    def sample(weights, n_samples, noise_std, rs):
        latents = rs.randn(latent_dimension, n_samples)
        latents = np.array(sorted(latents.T, key=(lambda a_entry: a_entry[0]))).T
        noise = (rs.randn(n_samples, observed_dimension) * noise_std)
        observed = (predict(weights, latents) + noise)
        return (latents, observed)
    def predict(weights, latents):
        return np.dot(weights, latents).T
    def logprob(weights, latents, noise_std, observed):
        preds = predict(weights, latents)
        log_lik = np.sum(t.logpdf(preds, 2.4, observed, noise_std))
        return log_lik
    num_weights = (observed_dimension * latent_dimension)
    def unpack_weights(weights):
        return np.reshape(weights, (observed_dimension, latent_dimension))
    return (num_weights, sample, logprob, unpack_weights)

What's a good function header?

Here's a good function header?
assistant
#Here's a good function header.

def make_ica_funs(observed_dimension, latent_dimension):