code: def _naive_batch_renormalization(
      x,
      gamma,
      beta,  # variables
      mean,
      var,  # variables
      running_mean,
      running_var,  # arrays
      rmax,
      dmax,
      eps,
      decay,
  ):
      # If decay is not None, the running stats are updated.
  
      F = chainer.functions
      assert isinstance(x, chainer.Variable)
      assert isinstance(gamma, chainer.Variable)
      assert isinstance(beta, chainer.Variable)
      assert isinstance(mean, chainer.Variable)
      assert isinstance(var, chainer.Variable)
      assert isinstance(running_mean, chainer.get_array_types())
      assert isinstance(running_var, chainer.get_array_types())
      assert mean.shape == var.shape
      assert mean.shape == running_mean.shape
      assert mean.shape == running_var.shape
      assert mean.shape == gamma.shape
  
      dt = x.dtype.type
      std = F.sqrt(var + dt(eps))
      # r and d are gradient-stopped
      running_std = numpy.sqrt(running_var + dt(eps))
      r = (std.array / running_std).clip(1.0 / rmax, rmax)
      d = ((mean.array - running_mean) / running_std).clip(-dmax, dmax)
      xhat = (x - mean) / std * r + d
      y = gamma * xhat + beta
  
      # Update running stats
      if decay is not None:
          running_mean *= decay
          running_mean += mean.array * dt(1.0 - decay)
          # unbiased estimation
          m = x.size // gamma.size
          adjust = m / max(m - 1.0, 1.0)
          running_var *= decay
          running_var += var.array * dt((1.0 - decay) * adjust)
  
      return y
  
imports: import numpy
  import six
  import chainer
  from chainer.functions.normalization import batch_renormalization
  from chainer import testing
  import chainerx
label_desc: Use image node_id 1 for calling a global function with example usage: _naive_batch_renormalization(x, gamma, beta, mean, var, running_mean, running_var, rmax, dmax, eps, decay) and returns: y