class_docstr: Multi Output Kernel class.
  
  This kernel can represent correlation between outputs of different datapoints.
  
  The `full_output_cov` argument holds whether the kernel should calculate
  the covariance between the outputs. In case there is no correlation but
  `full_output_cov` is set to True the covariance matrix will be filled with zeros
  until the appropriate size is reached.
class_docstr_tok: ["Multi","Output","Kernel","class",".","This","kernel","can","represent","correlation","between","outputs","of","different","datapoints",".","The","`","full_output_cov","`","argument","holds","whether","the","kernel","should","calculate","the","covariance","between","the","outputs",".","In","case","there","is","no","correlation","but","`","full_output_cov","`","is","set","to","True","the","covariance","matrix","will","be","filled","with","zeros","until","the","appropriate","size","is","reached","."]
docstr: The number of latent GPs in the multioutput kernel
docstr_tok: ["The","number","of","latent","GPs","in","the","multioutput","kernel"]
code: def num_latent_gps(self) -> int:
      """The number of latent GPs in the multioutput kernel"""
      raise NotImplementedError
  
imports: import abc
  from typing import Optional, Sequence, Tuple
  import tensorflow
  from check_shapes import check_shape
  from check_shapes import check_shapes, inherit_check_shapes
  from ...base import Parameter, TensorType
  from ..base import Combination, Kernel
label_desc: Use image node_id 1 for calling the MultioutputKernel obj's underlying member method code with example usage: obj.num_latent_gps() without return types