class_docstr: LayerNorm optimized for Apple Neural Engine (ANE) execution
  
  Note: This layer only supports normalization over the final dim. It expects `num_channels`
  as an argument and not `normalized_shape` which is used by `torch.nn.LayerNorm`.
class_docstr_tok: ["LayerNorm","optimized","for","Apple","Neural","Engine","(","ANE",")","execution","Note",":","This","layer","only","supports","normalization","over","the","final","dim",".","It","expects","`","num_channels","`","as","an","argument","and","not","`","normalized_shape","`","which","is","used","by","`","torch.nn.LayerNorm","`","."]
code: def forward(self, inputs):
      input_rank = len(inputs.size())
  
      # Principle 1: Picking the Right Data Format (machinelearning.apple.com/research/apple-neural-engine)
      # Migrate the data format from BSC to BC1S (most conducive to ANE)
      if input_rank == 3 and inputs.size(2) == self.num_channels:
          inputs = inputs.transpose(1, 2).unsqueeze(2)
          input_rank = len(inputs.size())
  
      assert input_rank == self.expected_rank
      assert inputs.size(1) == self.num_channels
  
      if self.clip_mag is not None:
          inputs.clamp_(-self.clip_mag, self.clip_mag)
  
      channels_mean = inputs.mean(dim=1, keepdims=True)
  
      zero_mean = inputs - channels_mean
  
      zero_mean_sq = zero_mean * zero_mean
  
      denom = (
          zero_mean_sq.mean(dim=1, keepdims=True) + self.eps
      ).rsqrt()
  
      out = zero_mean * denom
  
      if self.elementwise_affine:
          out = (
              out + self.bias.view(1, self.num_channels, 1, 1)
          ) * self.weight.view(1, self.num_channels, 1, 1)
  
      return out
  
imports: import torch
  import torch.nn
label_desc: Use image node_id 3 for calling the LayerNormANE obj's underlying member method code with example usage: obj.forward(inputs) and returns: out