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 __init__(
      self,
      input_size: int,
      output_size: int,
      layers: int = 3,
      layer_width: int = 14,
  ) -> None:
      assert layers >= 2
      self.input_size = input_size
      self.output_size = output_size
      self.layers = [np.zeros((layer_width, input_size))]
      for _ in range(layers - 2):
          self.layers += [np.zeros((layer_width, layer_width))]
      self.layers += [np.zeros((output_size, layer_width))]
      assert len(self.layers) == 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 1 to create a new Agent object  with example: obj = Agent(input_size, output_size, layers, layer_width)