code: def cob_entry_prob_distribution(size):
      full_size = int(np.prod(size))
      vals = torch.tensor([-1, 0, 1])
      probs = torch.tensor([0.0075, 0.985, 0.0075]).unsqueeze(0)
      cum_sum = torch.cumsum(probs, dim=-1)
      unif_prob = torch.rand((full_size, 1))
      tensor_idx = torch.argmax((unif_prob <= cum_sum).int(), dim=1)
      tensor = vals[tensor_idx]
      return tensor.reshape(size)
  
file_path: nebuly/optimization/open_alpha_tensor/open_alpha_tensor/core/data/basis_change.py
imports: from pathlib import Path
  from typing import Callable
  import numpy
  import torch
label_desc: Use image node_id 2 for calling a global function with example usage: cob_entry_prob_distribution(size) and returns: tensor