code: def fallback_independent_latent_conditional(
      Xnew: tf.Tensor,
      inducing_variable: MultioutputInducingVariables,
      kernel: IndependentLatent,
      f: tf.Tensor,
      *,
      full_cov: bool = False,
      full_output_cov: bool = False,
      q_sqrt: Optional[tf.Tensor] = None,
      white: bool = False,
  ) -> MeanAndVariance:
      """Interdomain conditional with independent latents.
      In this case the number of latent GPs (L) will be different than the number of outputs (P)
      The covariance matrices used to calculate the conditional have the following shape:
      - Kuu: [L, M, M]
      - Kuf: [M, L, N, P]
      - Kff: [N, P, N, P], [N, P, P], [N, P]
  
      Further reference:
  
      - See `gpflow.conditionals._conditional` for a detailed explanation of
        conditional in the single-output case.
      - See the multioutput notebook for more information about the multioutput framework.
      - See above for the parameters and the return value.
      """
      posterior = FallbackIndependentLatentPosterior(
          kernel,
          inducing_variable,
          f,
          q_sqrt,
          whiten=white,
          mean_function=None,
          precompute_cache=None,
      )
      return posterior.fused_predict_f(
          Xnew, full_cov=full_cov, full_output_cov=full_output_cov
      )
  
file_path: GPflow/gpflow/conditionals/multioutput/conditionals.py
imports: from typing import Optional
  import tensorflow
  from check_shapes import check_shapes
  from ...base import MeanAndVariance
  from ...inducing_variables import FallbackSeparateIndependentInducingVariables, FallbackSharedIndependentInducingVariables, InducingPoints, MultioutputInducingVariables, SeparateIndependentInducingVariables, SharedIndependentInducingVariables
  from ...kernels import IndependentLatent, LinearCoregionalization, MultioutputKernel, SeparateIndependent, SharedIndependent
  from ...posteriors import FallbackIndependentLatentPosterior, FullyCorrelatedPosterior, IndependentPosteriorMultiOutput, LinearCoregionalizationPosterior
  from ..dispatch import conditional
label_desc: Use image node_id 3 for calling a global function with example usage: fallback_independent_latent_conditional(Xnew, inducing_variable, kernel, f) and returns: posterior