In the policy gradient equation, is $\pi(a_{t} | s_{t}, \theta)$ a distribution or a function?

I am learning about policy gradient methods from the Deep RL Bootcamp by Peter Abbeel and I am a bit stumbled by the math presented. In the lecture, he derives the gradient logarithm likelihood of a trajectory to be

$$\nabla log P(\tau^{i};\theta) = \Sigma_{t=0}\nabla_{\theta}log\pi(a_{t}|s_t, \theta).$$

Is $$\pi(a_{t} | s_{t}, \theta)$$ a distribution or a function? Because a derivative can only be taken wrt a function. My understanding is that $$\pi(a_{t},s_{t}, \theta)$$ is usually represented as a distribution of actions over states, since input of a neural network for policy gradient would be the $$s_t$$ and output would be $$\pi(a_t, s_t)$$, using model weights $$\theta$$.

First, the derivative is usually taken with respect to a variable (input) of the function. Hence the notation $$\frac{df}{dx}$$ for some function $$f(x)$$.
$$\nabla log P(\tau^{i};\theta) = \Sigma_{t=0}\nabla_{\theta}log\pi(a_{t}|s_t, \theta).$$
You will see that the gradient is taken with respect to $$\theta$$, which are the parameters (i.e. a vector) e.g. of your neural network, that is, $$\nabla_{\theta}$$.
In this case, it doesn't really matter whether $$\pi$$ represents a distribution or not (for some specific value of $$\theta$$), but you're right that $$\pi$$ often represents a probability distribution over the possible actions (given a specific state). In any case, $$\pi$$ is a function of the parameters $$\theta$$ (i.e. in the case of a distribution, $$\pi_{\theta}$$ is a family of distributions for all possible values of $$\theta$$), i.e. if you change $$\theta$$ the outputs of $$\pi$$ also change, so you can take the derivative of it with respect to $$\theta$$.