code: def build_weights_dict(self, input_shape):
      # Input shape is anything (all flattened)
      input_size = np.prod(input_shape, dtype=int)
      self.parser = WeightsParser()
      self.parser.add_weights("params", (input_size, self.size))
      self.parser.add_weights("biases", (self.size,))
      return self.parser.N, (self.size,)
  
imports: from __future__ import absolute_import
  from __future__ import print_function
  from builtins import range
  import autograd.numpy
  import autograd.numpy.random
  import autograd.scipy.signal
  from autograd import grad
  import data_mnist
label_desc: Use image node_id 2 for calling the full_layer obj's underlying member method code with example usage: obj.build_weights_dict(input_shape) and returns: self,  self