class_docstr: Polynomial Kernel.
  
  Is based on the dot product covariance function and can be obtained
  from polynomial regression. This kernel, when parameterizing a
  Gaussian Process, results in random polynomial functions.
  A linear kernel can be created from this by setting the exponent to 1
  or None.
  
  ```none
  k(x, y) = bias_amplitude**2 + slope_amplitude**2 *
            ((x - shift) dot (y - shift))**exponent
  ```
  
  #### References
  
  [1]: Carl Edward Rasmussen and Christopher K. I. Williams. Gaussian
       Processes for Machine Learning. Section 4.4.2. 2006.
       http://www.gaussianprocess.org/gpml/chapters/RW4.pdf
  [2]: David Duvenaud. The Kernel Cookbook.
       https://www.cs.toronto.edu/~duvenaud/cookbook/
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docstr: Shift of linear function that is exponentiated.
docstr_tok: ["Shift","of","linear","function","that","is","exponentiated","."]
code: def shift(self):
      """Shift of linear function that is exponentiated."""
      return self._shift
  
imports: import tensorflow.compat.v2
  from tensorflow_probability.python.internal import assert_util
  from tensorflow_probability.python.internal import dtype_util
  from tensorflow_probability.python.internal import parameter_properties
  from tensorflow_probability.python.internal import prefer_static
  from tensorflow_probability.python.internal import tensor_util
  from tensorflow_probability.python.math.psd_kernels import positive_semidefinite_kernel
  from tensorflow_probability.python.math.psd_kernels.internal import util
label_desc: Use image node_id 5 for calling the Polynomial obj's underlying member method code with example usage: obj.shift() and returns: self