code: def mlp(
      input_dim,
      hidden_dim,
      output_dim,
      hidden_depth,
      output_mod=None,
      act=nn.ReLU,
  ):
      if hidden_depth == 0:
          mods = [nn.Linear(input_dim, output_dim)]
      else:
          mods = [nn.Linear(input_dim, hidden_dim), act()]
          for i in range(hidden_depth - 1):
              mods += [nn.Linear(hidden_dim, hidden_dim), act()]
          mods.append(nn.Linear(hidden_dim, output_dim))
      if output_mod is not None:
          mods.append(output_mod)
      trunk = nn.Sequential(*mods)
      return trunk
  
imports: import argparse
  import os
  import math
  import matplotlib.pyplot
  import torch
  import torch.nn
  from torchdiffeq import odeint, odeint_event
  from bouncing_ball import BouncingBallExample
label_desc: Use image node_id 1 for calling a global function with example usage: mlp(input_dim, hidden_dim, output_dim, hidden_depth, output_mod, act) and returns: trunk