class_docstr: Computes a covariance matrix based on the Polynomial kernel
  between inputs :math:`\mathbf{x_1}` and :math:`\mathbf{x_2}`:
  
  .. math::
      \begin{equation*}
          k_\text{Poly}(\mathbf{x_1}, \mathbf{x_2}) = (\mathbf{x_1}^\top
          \mathbf{x_2} + c)^{d}.
      \end{equation*}
  
  where
  
  * :math:`c` is an offset parameter.
  
  Args:
      offset_prior (:class:`gpytorch.priors.Prior`):
          Prior over the offset parameter (default `None`).
      offset_constraint (Constraint, optional):
          Constraint to place on offset parameter. Default: `Positive`.
      active_dims (list):
          List of data dimensions to operate on.
          `len(active_dims)` should equal `num_dimensions`.
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code: def offset(self, value: torch.Tensor) -> None:
      self._set_offset(value)
  
file_path: gpytorch/gpytorch/kernels/polynomial_kernel.py
imports: from typing import Optional
  import torch
  from ..constraints import Interval, Positive
  from ..priors import Prior
  from .kernel import Kernel
label_desc: Use image node_id 3 for calling the PolynomialKernel obj's underlying member method code with example usage: obj.offset(value) without return types