code: def _compress_gaussians(white_vec, prec_sqrt, inputs):
      dim, rank = prec_sqrt.shape[-2:]
      if rank <= dim:
          return None
      white_vec, prec_sqrt, shift = _compress_rank(white_vec, prec_sqrt)
      int_inputs = OrderedDict(
          (k, v) for k, v in inputs if v.dtype != "real"
      )
      return Gaussian(white_vec, prec_sqrt, inputs) + Tensor(
          shift, int_inputs
      )
  
imports: import math
  from collections import OrderedDict, defaultdict
  from contextlib import contextmanager
  from functools import reduce
  import funsor.ops
  from funsor.affine import affine_inputs, extract_affine, is_affine
  from funsor.delta import Delta
  from funsor.domains import Real, Reals
  from funsor.interpretations import compress_gaussians
  from funsor.ops import AddOp, SubOp
  from funsor.tensor import Tensor, align_tensor, align_tensors
  from funsor.terms import Binary, Funsor, FunsorMeta, Number, Slice, Subs, Variable, eager, reflect
  from funsor.util import broadcast_shape, get_tracing_state, lazy_property
label_desc: Use image node_id 16 for calling a global function with example usage: _compress_gaussians(white_vec, prec_sqrt, inputs) and returns: unknown, None