class_docstr: Shared definition of inducing variables for each independent latent process.
  
  This class is designated to be used to:
  
  - provide a general interface for multioutput kernels
    constructed from independent latent processes,
  - only require the specification of Kuu and Kuf.
  
  All multioutput kernels constructed from independent latent processes allow
  the inducing variables to be specified in the latent processes, and a
  reasonably efficient method (i.e. one that takes advantage of the
  independence in the latent processes) can be specified quite generally by
  only requiring the following covariances:
  
  - Kuu: [L, M, M],
  - Kuf: [L, M, N, P].
  
  In `gpflow/conditionals/multioutput/conditionals.py` we define a conditional() implementation
  for this combination. We specify this code path for all kernels which inherit from
  `IndependentLatentBase`. This set-up allows inference with any such kernel to be implemented by
  specifying only `Kuu()` and `Kuf()`.
  
  We call this the base class, since many multioutput GPs that are constructed
  from independent latent processes acutally allow even more efficient
  approximations. However, we include this code path, as it does not require
  specifying a new `conditional()` implementation.
  
  Here, we share the definition of inducing variables between all latent
  processes.
code: def inducing_variables(self) -> Tuple[InducingVariables]:
      return (self.inducing_variable,)
  
imports: from typing import Sequence, Tuple
  import tensorflow
  from check_shapes import Shape, check_shapes
  from ..inducing_variables import InducingVariables
label_desc: Use image node_id 3 for calling the FallbackSharedIndependentInducingVariables obj's underlying member method code with example usage: obj.inducing_variables() and returns: self