code: def __init__(self, num_features):
      super().__init__(
          num_features,
          track_running_stats=False,
          eps=1e-12,
          momentum=0.85,
          affine=True,
      )
      self.weight.requires_grad = False
      self.bias.requires_grad = True
  
file_path: tinygrad/examples/hlb_cifar10.py
imports: import random, time
  import numpy
  from typing import Optional
  from extra.datasets import fetch_cifar, cifar_mean, cifar_std
  from tinygrad import nn, dtypes, Tensor, Device, GlobalCounters, TinyJit
  from tinygrad.nn.state import get_state_dict, get_parameters
  from tinygrad.nn import optim
  from extra.lr_scheduler import OneCycleLR
  from tinygrad.helpers import Context, getenv
label_desc: Use image node_id 1 to create a new BatchNorm object from inherited base classes: nn with example: obj = BatchNorm(num_features)