class_docstr: The Arc-cosine family of kernels which mimics the computation in neural
  networks. The order parameter specifies the assumed activation function.
  The Multi Layer Perceptron (MLP) kernel is closely related to the ArcCosine
  kernel of order 0.
  
  The key reference is :cite:t:`NIPS2009_3628`.
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docstr: :param order: specifies the activation function of the neural network
    the function is a rectified monomial of the chosen order
  :param variance: the (initial) value for the variance parameter
  :param weight_variances: the (initial) value for the weight_variances parameter,
      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 bias_variance: the (initial) value for the bias_variance parameter
      defaults to 1.0
  :param active_dims: a slice or list specifying which columns of X are used
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code: def __init__(
      self,
      order: int = 0,
      variance: TensorType = 1.0,
      weight_variances: TensorType = 1.0,
      bias_variance: TensorType = 1.0,
      *,
      active_dims: Optional[ActiveDims] = None,
      name: Optional[str] = None,
  ) -> None:
      """
      :param order: specifies the activation function of the neural network
        the function is a rectified monomial of the chosen order
      :param variance: the (initial) value for the variance parameter
      :param weight_variances: the (initial) value for the weight_variances parameter,
          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 bias_variance: the (initial) value for the bias_variance parameter
          defaults to 1.0
      :param active_dims: a slice or list specifying which columns of X are used
      """
      super().__init__(active_dims=active_dims, name=name)
  
      if order not in self.implemented_orders:
          raise ValueError("Requested kernel order is not implemented.")
      self.order = order
  
      self.variance = Parameter(variance, transform=positive())
      self.bias_variance = Parameter(
          bias_variance, transform=positive()
      )
      self.weight_variances = Parameter(
          weight_variances, transform=positive()
      )
      self._validate_ard_active_dims(self.weight_variances)
  
imports: from typing import Optional
  import numpy
  import tensorflow
  from check_shapes import check_shape
  from check_shapes import check_shapes, inherit_check_shapes
  from ..base import AnyNDArray, Parameter, TensorType
  from ..utilities import positive, to_default_float
  from .base import ActiveDims, Kernel
label_desc: Use image node_id 1 to create a new ArcCosine object from inherited base classes: Kernel with example: obj = ArcCosine(order, variance, weight_variances, bias_variance)