docs: def fit(self, pixel_flux, data_placeholder, var_list, session, feed_dict={}):
          """
          Parameters
          ----------
          pixel_flux : ndarray
              The TPF-like pixel flux time series. The first dimension
              must represent time, and the remaining two dimensions
              must represent the spatial dimensions.
          data_placeholder : tf.placeholder
              A placeholder which will be used to pass the n-th time stamp
              to `self.optimizer.minimize`.
          var_list : list
              The list of parameters (as tensors) to optimize for.
          session : instance of tf.Session
          feed_dict : dict
              Dictionary of additional arguments used to feed the loss function.
          """
          opt_params = []
          cadences = range(pixel_flux.shape[0])
  
          for n in tqdm.tqdm(cadences):
              feed_dict[data_placeholder] = pixel_flux[n]
              self.optimizer.minimize(session=session, feed_dict=feed_dict)
              opt_params.append([session.run(var) for var in var_list])
  
          return opt_params