class_docstr: Computes an affine transformation y = x * scale + bias, either learned via adaptive weights, or fixed.
  Efficient alternative to LayerNorm where we can avoid computing the mean and variance of the input, and
  just rescale the output of the previous layer.
  
  Args:
      d_model (int): input dimension of layer.
      adaptive_scale (bool): whether to learn the affine transformation parameters or not. If set to False,
          the scale is fixed to 1 and bias to 0, effectively performing a No-Op on the input.
          This is done for export compatibility.
class_docstr_tok: ["Computes","an","affine","transformation","y","=","x","*","scale","+","bias",",","either","learned","via","adaptive","weights",",","or","fixed",".","Efficient","alternative","to","LayerNorm","where","we","can","avoid","computing","the","mean","and","variance","of","the","input",",","and","just","rescale","the","output","of","the","previous","layer",".","Args",":","d_model","(","int",")",":","input","dimension","of","layer",".","adaptive_scale","(","bool",")",":","whether","to","learn","the","affine","transformation","parameters","or","not",".","If","set","to","False",",","the","scale","is","fixed","to","1","and","bias","to","0",",","effectively","performing","a","No-Op","on","the","input",".","This","is","done","for","export","compatibility","."]
code: def forward(self, x):
      scale = self.scale.view(1, 1, -1)
      bias = self.bias.view(1, 1, -1)
      return x * scale + bias
  
imports: import torch
  from torch import nn
  from torch.nn import LayerNorm
  from nemo.collections.asr.parts.submodules.conformer_modules import ConformerConvolution, ConformerFeedForward
  from nemo.collections.asr.parts.submodules.multi_head_attention import MultiHeadAttention, RelPositionMultiHeadAttention
  from nemo.collections.common.parts import adapter_modules
  from nemo.core.classes.mixins import AccessMixin
  from nemo.core.classes.mixins.adapter_mixins import AdapterModuleMixin
label_desc: Use image node_id 2 for calling the ScaleBiasLayer obj's underlying member method code with example usage: obj.forward(x) and returns: unknown