class_docstr: A GP to be used in multi-fidelity settings. 
class_docstr_tok: ["A","GP","to","be","used","in","multi-fidelity","settings","."]
docstr: Constructor. ZZ, XX, YY are the fidelity points, domain points and labels
  respectively.
  mf_kernel is either a combined kernel or a namespace with the following fields:
  {scale, fidel_kernel, domain_kernel}.
docstr_tok: ["Constructor",".","ZZ",",","XX",",","YY","are","the","fidelity","points",",","domain","points","and","labels","respectively",".","mf_kernel","is","either","a","combined","kernel","or","a","namespace","with","the","following","fields",":","{","scale",",","fidel_kernel",",","domain_kernel","}","."]
code: def __init__(
      self, ZZ, XX, YY, mf_kernel, mean_func, noise_var, *args, **kwargs
  ):
      """Constructor. ZZ, XX, YY are the fidelity points, domain points and labels
      respectively.
      mf_kernel is either a combined kernel or a namespace with the following fields:
      {scale, fidel_kernel, domain_kernel}.
      """
      self.ZZ = list(ZZ)
      self.XX = list(XX)
      self.YY = list(YY)
      if not isinstance(mf_kernel, gp_kernel.Kernel):
          kernel = gp_kernel.CartesianProductKernel(
              mf_kernel.scale,
              [mf_kernel.fidel_kernel, mf_kernel.domain_kernel],
          )
          self.fidel_kernel = mf_kernel.fidel_kernel
          self.domain_kernel = mf_kernel.domain_kernel
      else:
          kernel = mf_kernel
      ZX = self.get_ZX_from_ZZ_XX(ZZ, XX)  # The 'X' data
      # Call super constructor
      super(MFGP, self).__init__(
          ZX, YY, kernel, mean_func, noise_var, *args, **kwargs
      )
  
imports: from __future__ import division
  from .None import kernel
  from .gp_core import GP, GPFitter, mandatory_gp_args
  from ..utils.option_handler import load_options
  from ..utils.reporters import get_reporter
  from ..utils.ancillary_utils import get_list_of_floats_as_str
label_desc: Use image node_id 1 to create a new MFGP object from inherited base classes: GP with example: obj = MFGP(ZZ, XX, YY, mf_kernel, mean_func, noise_var)