docstr: Equivalent kernel of a neural network with L hidden layers and
  a cosine or sine activation function.
  
  Args:
      input_dim (int): Dimensionality of input.
      variance_w (float): Variance of the weights.
      mean_w (float): Mean of the weights.
      variance_b (float): Variance of the biases.
      mean_b (float): Mean of the biases.
      L (int): The number of hidden layers.
      cos_or_sin (str): 'cos' for cosine, 'sin' for sine, 'half' for
          half of the activations sqrt(2)*sin and the other half 
          sqrt(2)*cos
code: def __init__(
      self,
      input_dim,
      variance_w=1.0,
      mean_w=0,
      variance_b=0,
      mean_b=0,
      L=1,
      name="nn_lrelu",
      standard_first_layer=False,
      cos_or_sin="cos",
  ):
      """
      Equivalent kernel of a neural network with L hidden layers and
      a cosine or sine activation function.
  
      Args:
          input_dim (int): Dimensionality of input.
          variance_w (float): Variance of the weights.
          mean_w (float): Mean of the weights.
          variance_b (float): Variance of the biases.
          mean_b (float): Mean of the biases.
          L (int): The number of hidden layers.
          cos_or_sin (str): 'cos' for cosine, 'sin' for sine, 'half' for
              half of the activations sqrt(2)*sin and the other half
              sqrt(2)*cos
      """
      super().__init__(
          input_dim,
          variance_w,
          mean_w,
          variance_b,
          mean_b,
          L,
          name,
          standard_first_layer,
      )
      if cos_or_sin == "cos":
          self.trig = np.cos
      elif cos_or_sin == "sin":
          self.trig = np.sin
      self.cos_or_sin = cos_or_sin
  
file_path: elu-gelu-kernels/code/kernels/nn_kernel_trig.py
imports: import numpy
  from scipy.stats import multivariate_normal
  from scipy.stats import norm
  from scipy.special import erf
  import GPy
  import abc
  from .nn_kernel import NNKernel
label_desc: Use image node_id 1 to create a new NNKernelTrig object from inherited base classes: NNKernel with example: obj = NNKernelTrig(input_dim, variance_w, mean_w, variance_b, mean_b, L, name, standard_first_layer, cos_or_sin)