def forward_log_det_jacobian(self,<EOL>x,<EOL>event_ndims,<EOL>name="<STR_LIT>",<EOL>**kwargs):
return self._call_forward_log_det_jacobian(x, event_ndims, name, **kwargs)<EOL>
Returns both the forward_log_det_jacobian.

        Args:
          x: `Tensor`. The input to the "forward" Jacobian determinant evaluation.
          event_ndims: Number of dimensions in the probabilistic events being
            transformed. Must be greater than or equal to
            `self.forward_min_event_ndims`. The result is summed over the final
            dimensions to produce a scalar Jacobian determinant for each event, i.e.
            it has shape `rank(x) - event_ndims` dimensions.
          name: The name to give this op.
          **kwargs: Named arguments forwarded to subclass implementation.

        Returns:
          `Tensor`, if this bijector is injective.
            If not injective this is not implemented.

        Raises:
          TypeError: if `self.dtype` is specified and `y.dtype` is not
            `self.dtype`.
          NotImplementedError: if neither `_forward_log_det_jacobian`
            nor {`_inverse`, `_inverse_log_det_jacobian`} are implemented, or
            this is a non-injective bijector.