Get the number of intrinsics parameters in the optimization vector

SYNOPSIS

    m = mrcal.cameramodel('xxx.cameramodel')

    optimization_inputs = m.optimization_inputs()

    b = mrcal.optimizer_callback(**optimization_inputs)[0]
    mrcal.unpack_state(b, **optimization_inputs)

    i_state0 = mrcal.state_index_intrinsics(0, **optimization_inputs)
    Nstates  = mrcal.num_states_intrinsics (   **optimization_inputs)

    intrinsics_all = b[i_state0:i_state0+Nstates]

The optimization algorithm sees its world described in one, big vector of state.
The optimizer doesn't know or care about the meaning of each element of this
vector, but for later analysis, it is useful to know what's what. The
mrcal.num_states_...() functions report how many variables in the optimization
vector are taken up by each particular kind of measurement.

THIS function reports how many optimization variables are used to represent ALL
the camera intrinsics. The intrinsics are stored contiguously. They consist of a
4-element "intrinsics core" (focallength-x, focallength-y, centerpixel-x,
centerpixel-y) followed by a lensmodel-specific vector of "distortions". A
similar function mrcal.num_intrinsics_optimization_params() is available to
report the number of optimization variables used for just ONE camera. If all the
intrinsics are being optimized, then the mrcal.lensmodel_num_params() returns
the same value: the number of values needed to describe the intrinsics of a
single camera. It is possible to lock down some of the intrinsics during
optimization (by setting the do_optimize_intrinsics_... variables
appropriately). These variables control what
mrcal.num_intrinsics_optimization_params() and mrcal.num_states_intrinsics()
return, but not mrcal.lensmodel_num_params().

In order to determine the variable mapping, we need quite a bit of context. If
we have the full set of inputs to the optimization function, we can pass in
those (as shown in the example above). Or we can pass the individual arguments
that are needed (see ARGUMENTS section for the full list). If the optimization
inputs and explicitly-given arguments conflict about the size of some array, the
explicit arguments take precedence. If any array size is not specified, it is
assumed to be 0. Thus most arguments are optional.

ARGUMENTS

- **kwargs: if the optimization inputs are available, they can be passed-in as
  kwargs. These inputs contain everything this function needs to operate. If we
  don't have these, then the rest of the variables will need to be given

- lensmodel: string specifying the lensmodel we're using (this is always
  'LENSMODEL_...'). The full list of valid models is returned by
  mrcal.supported_lensmodels(). This is required if we're not passing in the
  optimization inputs

- do_optimize_intrinsics_core
  do_optimize_intrinsics_distortions
  do_optimize_extrinsics
  do_optimize_calobject_warp
  do_optimize_frames

  optional booleans; default to True. These specify what we're optimizing. See
  the documentation for mrcal.optimize() for details

- Ncameras_intrinsics
  Ncameras_extrinsics
  Nframes
  Npoints
  Npoints_fixed

  optional integers; default to 0. These specify the sizes of various arrays in
  the optimization. See the documentation for mrcal.optimize() for details

RETURNED VALUE

The integer reporting the variable count of intrinsics in the state vector
