def fit(self, pixel_flux, data_placeholder, var_list, session, feed_dict={}):
opt_params = []<EOL>cadences = range(pixel_flux.shape[<NUM_LIT:0>])<EOL>for n in tqdm.tqdm(cadences):<EOL><INDENT>feed_dict[data_placeholder] = pixel_flux[n]<EOL>self.optimizer.minimize(session=session, feed_dict=feed_dict)<EOL>opt_params.append([session.run(var) for var in var_list])<EOL><DEDENT>return opt_params<EOL>
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.