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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 chosen_probabs(probab_observations, actions):
 """Picks out the probabilities of the actions along batch and time-steps. Args: probab_observations: ndarray of shape `[B, T+1, A]`, where probab_observations[b, t, i] contains the log-probability of action = i at the t^th time-step in the b^th trajectory. actions: ndarray of shape `[B, T]`, with each entry in [0, A) denoting which action was chosen in the b^th trajectory's t^th time-step. Returns: `[B, T]` ndarray with the log-probabilities of the chosen actions. """

  B, T = actions.shape  # pylint: disable=invalid-name
  assert (B, T + 1) == probab_observations.shape[:2]
  return probab_observations[np.arange(B)[:, None], np.arange(T), actions]