code: def __call__(self, x: Tensor):
      if Tensor.training:
          # This requires two full memory accesses to x
          # https://github.com/pytorch/pytorch/blob/c618dc13d2aa23625cb0d7ada694137532a4fa33/aten/src/ATen/native/cuda/Normalization.cuh
          # There's "online" algorithms that fix this, like https://en.wikipedia.org/wiki/Algorithms_for_calculating_variance#Welford's_Online_algorithm
          batch_mean = x.mean(axis=(0, 2, 3))
          y = x - batch_mean.reshape(shape=[1, -1, 1, 1])
          batch_var = (y * y).mean(axis=(0, 2, 3))
          batch_invstd = batch_var.add(self.eps).pow(-0.5)
  
          # NOTE: wow, this is done all throughout training in most PyTorch models
          if self.track_running_stats:
              self.running_mean.assign(
                  (1 - self.momentum) * self.running_mean
                  + self.momentum * batch_mean.detach()
              )
              self.running_var.assign(
                  (1 - self.momentum) * self.running_var
                  + self.momentum
                  * prod(y.shape)
                  / (prod(y.shape) - y.shape[1])
                  * batch_var.detach()
              )
              self.num_batches_tracked += 1
      else:
          batch_mean = self.running_mean
          # NOTE: this can be precomputed for static inference. we expand it here so it fuses
          batch_invstd = (
              self.running_var.reshape(1, -1, 1, 1)
              .expand(x.shape)
              .add(self.eps)
              .rsqrt()
          )
  
      return x.batchnorm(
          self.weight, self.bias, batch_mean, batch_invstd
      )
  
imports: import math
  from typing import Optional, Union, Tuple, cast
  from tinygrad.tensor import Tensor
  from tinygrad.helpers import prod
  from tinygrad.nn import optim, state
label_desc: Use image node_id 2 for calling the BatchNorm2d obj's underlying member method code with example usage: obj.__call__(x) and returns: x