code: def log_likelihood(x: Tensor, mu: Tensor, log_sigma: Tensor):
      # print(x.shape, mu.shape, log_sigma.shape)
      # return (x-mu).abs() * (-log_sigma).exp() + log_sigma
      return (x - mu).square() * (-2 * log_sigma).exp() / 2 + log_sigma
  
imports: import sys, sqlite3, pickle, math
  from collections import defaultdict
  from tqdm import tqdm, trange
  import numpy
  from tinygrad.ops import LazyOp, TernaryOps, BinaryOps, UnaryOps, ReduceOps, BufferOps, MemBuffer, ConstBuffer
  from tinygrad.dtype import dtypes
  from tinygrad.shape.shapetracker import ShapeTracker
  from tinygrad.shape.view import View
  from tinygrad.shape.symbolic import Variable
  from tinygrad.codegen.kernel import Opt, OptOps
  from tinygrad.codegen.linearizer import Linearizer
  from tinygrad.features.search import actions
  from extra.optimization.helpers import lin_to_feats
  from extra.optimization.pretrain_valuenet import ValueNet
  from tinygrad.nn.optim import Adam
  from tinygrad.nn.state import get_parameters, get_state_dict, safe_save, safe_load, load_state_dict
  import random
  from tinygrad.tensor import Tensor
  from tinygrad.helpers import getenv
label_desc: Use image node_id 2 for calling a global function with example usage: log_likelihood(x, mu, log_sigma) and returns: unknown