class_docstr: Implementation of random fourier feature map for feature processing.
  
  Applies sqrt(2 / output_dims) * cos(wx+b), where:
      output_dims is the output feature dimensions, and
      wx + b applies FC using randomized, fixed weight and bias parameters
  
  For more information, see the original paper:
      https://people.eecs.berkeley.edu/~brecht/papers/07.rah.rec.nips.pdf
  
  Inputs:
      output_dims -- output feature dimensions
      sigma -- bandwidth for the Gaussian kernel estimator
      w_init -- initialization options for weight parameter
      b_init -- initialization options for bias parameter
code: def add_ops(self, net):
      # Random features: wx + b
      cosine_arg = net.FC(
          self.input_record.field_blobs() + [self.w, self.b],
          net.NextScopedBlob("cosine_arg"),
      )
  
      # Apply cosine to new vectors
      new_feature_vec = net.Cos(
          [cosine_arg], net.NextScopedBlob("new_feature_vec")
      )
  
      # Multiply each element in vector by sqrt(2/D)
      scale = np.sqrt(2.0 / self.output_dims)
      net.Scale(
          [new_feature_vec],
          self.output_schema.field_blobs(),
          scale=scale,
      )
  
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 RandomFourierFeatures obj's underlying member method code with example usage: obj.add_ops(net) without return types