class_docstr: Linear Kernel.
  
  Is based on the dot product covariance function and can be obtained
  from linear regression. This kernel, when parameterizing a
  Gaussian Process, results in random linear functions.
  The Linear kernel is based on the Polynomial kernel without the
  exponent.
  
  ```none
  k(x, y) = bias_amplitude**2 + slope_amplitude**2 *
            ((x - shift) dot (y - shift))
  ```
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docstr: Construct a Linear kernel instance.
  
  Args:
    bias_amplitude: Non-negative floating point `Tensor` that controls the
      stddev from the origin. If bias = 0, there is no stddev and the
      fitted function goes through the origin.  Must be broadcastable with
      `slope_amplitude`, `shift`, `exponent`, and inputs to `apply` and
      `matrix` methods. A value of `None` is treated like 0.
      Default Value: `None`
    slope_amplitude: Non-negative floating point `Tensor` that controls the
      stddev of the regression line slope that is the basis for the
      polynomial. Must be broadcastable with `bias_amplitude`, `shift`,
      `exponent`, and inputs to `apply` and `matrix` methods. A value of
      `None` is treated like 1.
      Default Value: `None`
    shift: Floating point `Tensor` that controls the intercept with the x-axis
      of the linear interpolation. Must be broadcastable with
      `bias_amplitude`, `slope_amplitude`, and inputs to `apply` and `matrix`
      methods. A value of `None` is treated like 0, which results in having
      the intercept at the origin.
    feature_ndims: Python `int` number of rightmost dims to include in kernel
      computation.
      Default Value: 1
    validate_args: If `True`, parameters are checked for validity despite
      possibly degrading runtime performance.
      Default Value: `False`
    parameters: For subclasses, a dict of constructor arguments.
    name: Python `str` name prefixed to Ops created by this class.
      Default Value: `'Linear'`
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code: def __init__(
      self,
      bias_amplitude=None,
      slope_amplitude=None,
      shift=None,
      feature_ndims=1,
      validate_args=False,
      parameters=None,
      name="Linear",
  ):
      """Construct a Linear kernel instance.
  
      Args:
        bias_amplitude: Non-negative floating point `Tensor` that controls the
          stddev from the origin. If bias = 0, there is no stddev and the
          fitted function goes through the origin.  Must be broadcastable with
          `slope_amplitude`, `shift`, `exponent`, and inputs to `apply` and
          `matrix` methods. A value of `None` is treated like 0.
          Default Value: `None`
        slope_amplitude: Non-negative floating point `Tensor` that controls the
          stddev of the regression line slope that is the basis for the
          polynomial. Must be broadcastable with `bias_amplitude`, `shift`,
          `exponent`, and inputs to `apply` and `matrix` methods. A value of
          `None` is treated like 1.
          Default Value: `None`
        shift: Floating point `Tensor` that controls the intercept with the x-axis
          of the linear interpolation. Must be broadcastable with
          `bias_amplitude`, `slope_amplitude`, and inputs to `apply` and `matrix`
          methods. A value of `None` is treated like 0, which results in having
          the intercept at the origin.
        feature_ndims: Python `int` number of rightmost dims to include in kernel
          computation.
          Default Value: 1
        validate_args: If `True`, parameters are checked for validity despite
          possibly degrading runtime performance.
          Default Value: `False`
        parameters: For subclasses, a dict of constructor arguments.
        name: Python `str` name prefixed to Ops created by this class.
          Default Value: `'Linear'`
      """
      parameters = dict(locals()) if parameters is None else parameters
      super(Linear, self).__init__(
          bias_amplitude=bias_amplitude,
          slope_amplitude=slope_amplitude,
          shift=shift,
          exponent=None,
          feature_ndims=feature_ndims,
          validate_args=validate_args,
          parameters=parameters,
          name=name,
      )
  
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 1 to create a new Linear object from inherited base classes: Polynomial with example: obj = Linear(bias_amplitude, slope_amplitude, shift, feature_ndims, validate_args, parameters, name)