class_docstr: RmsNorm w/ fast (apex) norm if available
      
class_docstr_tok: ["RmsNorm","w\/","fast","(","apex",")","norm","if","available"]
code: def __init__(
      self, channels, eps=1e-6, affine=True, device=None, dtype=None
  ) -> None:
      factory_kwargs = {"device": device, "dtype": dtype}
      super().__init__()
      normalized_shape = channels
      if isinstance(normalized_shape, numbers.Integral):
          # mypy error: incompatible types in assignment
          normalized_shape = (normalized_shape,)  # type: ignore[assignment]
      self.normalized_shape = tuple(normalized_shape)  # type: ignore[arg-type]
      self.eps = eps
      self.elementwise_affine = affine
      if self.elementwise_affine:
          self.weight = nn.Parameter(
              torch.empty(self.normalized_shape, **factory_kwargs)
          )
      else:
          self.register_parameter("weight", None)
  
      self.reset_parameters()
  
imports: import numbers
  from typing import Tuple
  import torch
  import torch.nn
  import torch.nn.functional
  from .fast_norm import is_fast_norm, fast_group_norm, fast_layer_norm, fast_rms_norm
label_desc: Use image node_id 1 to create a new RmsNorm object from inherited base classes: nn with example: obj = RmsNorm(channels, eps, affine, device, dtype)