class_docstr: An agent has an input size, an output size, a number of layers, a width of its internal layers
  (a.k.a number of neurons per hidden layer).
class_docstr_tok: ["An","agent","has","an","input","size",",","an","output","size",",","a","number","of","layers",",","a","width","of","its","internal","layers","(","a.k.a","number","of","neurons","per","hidden","layer",")","."]
code: def dimension(self) -> int:
      return sum(layer.size for layer in self.layers)
  
file_path: nevergrad/nevergrad/functions/powersystems/core.py
imports: import typing
  from math import pi, cos, sin
  import matplotlib.pyplot
  import numpy
  from nevergrad.parametrization import parameter
  from ..base import ExperimentFunction
label_desc: Use image node_id 2 for calling the Agent obj's underlying member method code with example usage: obj.dimension() and returns: sum