code: def test_rmsnorm(self):
      from extra.models.llama import RMSNorm
  
      B, T, embed_size = 4, 10, 20
  
      layer_norm = RMSNorm(embed_size)
      x = Tensor.rand((B, T, embed_size)).contiguous().realize()
      y = layer_norm(x)
  
      # for norm layers, the correct way to shard weights is duplication
      layer_norm_sharded = RMSNorm(embed_size)
      layer_norm_sharded.weight.shard_((d0, d1), axis=None).realize()
  
      # if x is being sharded, then all-reduce is involved
      x_sharded = x.shard((d0, d1), axis=2).realize()
      y_shard = layer_norm_sharded(x_sharded).realize()
      np.testing.assert_allclose(
          y.numpy(), y_shard.numpy(), atol=1e-6, rtol=1e-6
      )
  
      # if x is being duplicated, then the operations remain inside each GPU
      # which is the common case
      x_sharded = x.shard((d0, d1), axis=None).realize()
      y_shard = layer_norm_sharded(x_sharded).realize()
      np.testing.assert_allclose(
          y.numpy(), y_shard.numpy(), atol=1e-6, rtol=1e-6
      )
  
imports: import functools
  import unittest
  from tinygrad import Tensor, Device, nn, GlobalCounters
  from tinygrad.device import _BufferCopy
  from tinygrad.helpers import CI
  from tinygrad.jit import TinyJit
  from tinygrad.nn.state import get_parameters
  import numpy
label_desc: Use image node_id 37 for calling the TestMultiTensor obj's underlying member method code with example usage: obj.test_rmsnorm() without return types