code: def forward(
      ctx, input, weight, normalized_shape, eps, memory_efficient=False
  ):
      global fused_layer_norm_cuda
      if fused_layer_norm_cuda is None:
          fused_layer_norm_cuda = importlib.import_module(
              "fused_layer_norm_cuda"
          )
      ctx.normalized_shape = normalized_shape
      ctx.eps = eps
      ctx.memory_efficient = memory_efficient
      input_ = input.contiguous()
      weight_ = weight.contiguous()
      output, invvar = fused_layer_norm_cuda.rms_forward_affine(
          input_, ctx.normalized_shape, weight_, ctx.eps
      )
      if ctx.memory_efficient:
          ctx.save_for_backward(output, weight_, invvar)
      else:
          ctx.save_for_backward(input_, weight_, invvar)
      return output
  
imports: import importlib
  import numbers
  import torch
  from torch.nn.parameter import Parameter
  from torch.nn import init
  from torch.nn import functional
  from apex._autocast_utils import _cast_if_autocast_enabled
label_desc: Use image node_id 1 for calling the FusedRMSNormAffineFunction obj's underlying member method code with example usage: obj.forward(ctx, input, weight, normalized_shape, eps, memory_efficient) and returns: output