code: def f_prob_distribution(size):
      """Samples a tensor of values from a distribution with a peak at 0 and a
      tail at -2 and 2.
  
      Args:
          size (int): Number of values to sample.
      """
      f_vals = torch.tensor([-2, -1, 0, 1, 2])
      f_probs = torch.tensor(
          [0.001, 0.099, 0.8, 0.099, 0.001]
      ).unsqueeze(0)
      f_cum_sum = torch.cumsum(f_probs, dim=-1)
      unif_prob = torch.rand((size, 1))
      tensor_idx = torch.argmax((unif_prob <= f_cum_sum).int(), dim=1)
      tensor = f_vals[tensor_idx]
      return tensor
  
imports: from typing import Callable
  import torch
label_desc: Use image node_id 2 for calling a global function with example usage: f_prob_distribution(size) and returns: tensor