docstr: Equivalent kernel of a neural network with L hidden layers and
  a GELU activation function.
  
  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.
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code: def __init__(
      self,
      input_dim,
      variance_w=1.0,
      mean_w=0,
      variance_b=0,
      mean_b=0,
      L=1,
      name="nn_gelu",
      standard_first_layer=False,
  ):
      """
      Equivalent kernel of a neural network with L hidden layers and
      a GELU activation function.
  
      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.
      """
      super().__init__(
          input_dim,
          variance_w,
          mean_w,
          variance_b,
          mean_b,
          L,
          name,
          standard_first_layer,
      )
  
file_path: elu-gelu-kernels/code/kernels/nn_kernel_gelu.py
imports: import numpy
  import GPy
  import abc
  from .nn_kernel import NNKernel
label_desc: Use image node_id 1 to create a new NNKernelGelu object from inherited base classes: NNKernel with example: obj = NNKernelGelu(input_dim, variance_w, mean_w, variance_b, mean_b, L, name, standard_first_layer)