class_docstr: The Gaussian Process Latent Variable Model (GPLVM) class for unsupervised learning.
  The class supports
  
  1. Point estimates for latent X when prior_x = None
  2. MAP Inference for X when prior_x is not None and inference == 'map'
  3. Gaussian variational distribution q(X) when prior_x is not None and inference == 'variational'
  
  .. seealso::
      The `GPLVM tutorial
      <examples/04_Variational_and_Approximate_GPs/Gaussian_Process_Latent_Variable_Models_with_Stochastic_Variational_Inference.ipynb>`_
      for use instructions.
  
  :param X: An instance of a sub-class of the LatentVariable class. One of,
      :class:`~gpytorch.models.gplvm.PointLatentVariable`, :class:`~gpytorch.models.gplvm.MAPLatentVariable`, or
      :class:`~gpytorch.models.gplvm.VariationalLatentVariable`, to facilitate inference with 1, 2, or 3 respectively.
  :type X: ~gpytorch.models.LatentVariable
  :param ~gpytorch.variational._VariationalStrategy variational_strategy: The strategy that determines
      how the model marginalizes over the variational distribution (over inducing points)
      to produce the approximate posterior distribution (over data)
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code: def __init__(self, X, variational_strategy):
      super().__init__(variational_strategy)
  
      # Assigning Latent Variable
      self.X = X
  
imports: from ..approximate_gp import ApproximateGP
label_desc: Use image node_id 1 to create a new BayesianGPLVM object from inherited base classes: ApproximateGP with example: obj = BayesianGPLVM(X, variational_strategy)