class_docstr: The Polynomial kernel. Functions drawn from a GP with this kernel are
  polynomials of degree `d`. The kernel equation is
  
      k(x, y) = (σ²xy + γ)ᵈ
  
  where:
  σ² is the variance parameter,
  γ is the offset parameter,
  d is the degree parameter.
class_docstr_tok: ["The","Polynomial","kernel",".","Functions","drawn","from","a","GP","with","this","kernel","are","polynomials","of","degree","`","d","`",".","The","kernel","equation","is","k","(","x",",","y",")","=","(","\u03c3\u00b2xy","+","\u03b3",")","\u1d48","where",":","\u03c3\u00b2","is","the","variance","parameter",",","\u03b3","is","the","offset","parameter",",","d","is","the","degree","parameter","."]
code: def K_diag(self, X: TensorType) -> tf.Tensor:
      return (super().K_diag(X) + self.offset) ** self.degree
  
file_path: GPflow/gpflow/kernels/linears.py
imports: from typing import Optional
  import tensorflow
  from check_shapes import check_shapes, inherit_check_shapes
  from ..base import Parameter, TensorType
  from ..utilities import positive
  from .base import ActiveDims, Kernel
label_desc: Use image node_id 3 for calling the Polynomial obj's underlying member method code with example usage: obj.K_diag(X) and returns: unknown