class_docstr: A general version of the arc-cosine kernel feature map (s = 1 restores
  the original arc-cosine kernel feature map).
  
  Applies H(x) * x^s, where H is the Heaviside step function and x is the
  input after applying FC (such that x = w * x_orig + b).
  
  For more information, see the original paper:
      http://cseweb.ucsd.edu/~saul/papers/nips09_kernel.pdf
  
  Inputs :
      output_dims -- dimensions of the output vector
      s -- degree to raise transformed features
      scale -- amount to scale the standard deviation
      weight_init -- initialization distribution for weight parameter
      bias_init -- initialization distribution for bias pararmeter
      weight_optim -- optimizer for weight params; None for random features
      bias_optim -- optimizer for bias param; None for random features
      set_weight_as_global_constant -- if True, initialized random parameters
                                       will be constant across all distributed
                                       instances of the layer
      initialize_output_schema -- if True, initialize output schema as Scalar
                                  from Arc Cosine; else output schema is None
docstr: Initializes the Layer Parameters for weight and bias terms for features
  
  Inputs :
      w_blob -- blob to contain w values
      b_blob -- blob to contain b values
      w_init -- initialization distribution for weight parameter
      b_init -- initialization distribution for bias parameter
      w_optim -- optimizer to use for w; if None, then will use no optimizer
      b_optim -- optimizer to user for b; if None, then will use no optimizer
docstr_tok: ["Initializes","the","Layer","Parameters","for","weight","and","bias","terms","for","features","Inputs",":","w_blob","--","blob","to","contain","w","values","b_blob","--","blob","to","contain","b","values","w_init","--","initialization","distribution","for","weight","parameter","b_init","--","initialization","distribution","for","bias","parameter","w_optim","--","optimizer","to","use","for","w",";","if","None",",","then","will","use","no","optimizer","b_optim","--","optimizer","to","user","for","b",";","if","None",",","then","will","use","no","optimizer"]
code: def _initialize_params(
      self,
      w_name,
      b_name,
      w_init=None,
      b_init=None,
      w_optim=None,
      b_optim=None,
  ):
      """
      Initializes the Layer Parameters for weight and bias terms for features
  
      Inputs :
          w_blob -- blob to contain w values
          b_blob -- blob to contain b values
          w_init -- initialization distribution for weight parameter
          b_init -- initialization distribution for bias parameter
          w_optim -- optimizer to use for w; if None, then will use no optimizer
          b_optim -- optimizer to user for b; if None, then will use no optimizer
      """
  
      w_init = (
          w_init
          if w_init
          else ("GaussianFill", {"mean": 0.0, "std": self.stddev})
      )
      w_optim = w_optim if w_optim else self.model.NoOptim
  
      b_init = (
          b_init
          if b_init
          else (
              "UniformFill",
              {"min": -0.5 * self.stddev, "max": 0.5 * self.stddev},
          )
      )
      b_optim = b_optim if b_optim else self.model.NoOptim
  
      w_param = self.create_param(
          param_name=w_name,
          shape=(self.output_dims, self.input_dims),
          initializer=w_init,
          optimizer=w_optim,
      )
  
      b_param = self.create_param(
          param_name=b_name,
          shape=[self.output_dims],
          initializer=b_init,
          optimizer=b_optim,
      )
  
      return [w_param, b_param]
  
file_path: pytorch/caffe2/python/layers/arc_cosine_feature_map.py
imports: from caffe2.python import schema
  from caffe2.python.layers.layers import ModelLayer
  import numpy
label_desc: Use image node_id 2 for calling the ArcCosineFeatureMap obj's underlying member method code with example usage: obj._initialize_params(w_name, b_name, w_init, b_init, w_optim, b_optim) and returns: list