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.
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docstr: Returns the correlation of f(X) and f(X), where f(.) can be multi-dimensional.
  
  :param X: data matrix
  :param full_output_cov: calculate correlation between outputs.
  :return: var[f(X)]
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code: def K_diag(
      self, X: TensorType, full_output_cov: bool = True
  ) -> tf.Tensor:
      """
      Returns the correlation of f(X) and f(X), where f(.) can be multi-dimensional.
  
      :param X: data matrix
      :param full_output_cov: calculate correlation between outputs.
      :return: var[f(X)]
      """
      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 4 for calling the MultioutputKernel obj's underlying member method code with example usage: obj.K_diag(X, full_output_cov) without return types