models/spatial/attncnf.py [101:122]:
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    def logprob(self, event_times, spatial_locations, input_mask=None, aux_state=None):
        return self._cond_logliks(event_times, spatial_locations, input_mask, aux_state)

    def _cond_logliks(self, event_times, spatial_locations, input_mask=None, aux_state=None):
        """
        Args:
            event_times: (N, T)
            spatial_locations: (N, T, D)
            input_mask: (N, T) or None
            aux_state: (N, T, D_a)

        Returns:
            A tensor of shape (N, T) containing the conditional log probabilities.
        """

        if input_mask is None:
            input_mask = torch.ones_like(event_times)

        assert event_times.shape == input_mask.shape
        assert event_times.shape[:2] == spatial_locations.shape[:2]
        if aux_state is not None:
            assert event_times.shape[:2] == aux_state.shape[:2]
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models/spatial/jumpcnf.py [54:75]:
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    def logprob(self, event_times, spatial_locations, input_mask=None, aux_state=None):
        return self._cond_logliks(event_times, spatial_locations, input_mask, aux_state)

    def _cond_logliks(self, event_times, spatial_locations, input_mask=None, aux_state=None):
        """
        Args:
            event_times: (N, T)
            spatial_locations: (N, T, D)
            input_mask: (N, T) or None
            aux_state: (N, T, D_a)

        Returns:
            A tensor of shape (N, T) containing the conditional log probabilities.
        """

        if input_mask is None:
            input_mask = torch.ones_like(event_times)

        assert event_times.shape == input_mask.shape
        assert event_times.shape[:2] == spatial_locations.shape[:2]
        if aux_state is not None:
            assert event_times.shape[:2] == aux_state.shape[:2]
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