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.
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docstr: :param degree: the degree of the polynomial
  :param variance: the (initial) value for the variance parameter(s),
      to induce ARD behaviour this must be initialised as an array the same
      length as the the number of active dimensions e.g. [1., 1., 1.]
  :param offset: the offset of the polynomial
  :param active_dims: a slice or list specifying which columns of X are used
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code: def __init__(
      self,
      degree: TensorType = 3.0,
      variance: TensorType = 1.0,
      offset: TensorType = 1.0,
      active_dims: Optional[ActiveDims] = None,
  ) -> None:
      """
      :param degree: the degree of the polynomial
      :param variance: the (initial) value for the variance parameter(s),
          to induce ARD behaviour this must be initialised as an array the same
          length as the the number of active dimensions e.g. [1., 1., 1.]
      :param offset: the offset of the polynomial
      :param active_dims: a slice or list specifying which columns of X are used
      """
      super().__init__(variance, active_dims)
      self.degree = degree
      self.offset = Parameter(offset, transform=positive())
  
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 1 to create a new Polynomial object from inherited base classes: Linear with example: obj = Polynomial(degree, variance, offset, active_dims)