class_docstr: Initialize the DeepSpeed Transformer Layer.
  
  Static variable:
      layer_id: The layer-index counter starting from 0 and incrementing by 1 every time a layer object is instantiated,
      e.g. if a model has 24 transformer layers, layer_id goes from 0 to 23.
  Arguments:
      config: An object of DeepSpeedTransformerConfig
  
      initial_weights: Optional: Only used for unit test
  
      initial_biases: Optional: Only used for unit test
code: def init_transformer_weights(self, adjust_init_range=False):
      num_layers = self.config.num_hidden_layers
      output_std = self.config.initializer_range
      if adjust_init_range and self.config.local_rank == 0:
          print("Accounting for accumulation on the residual path")
          output_std = self.config.initializer_range / math.sqrt(
              2.0 * num_layers
          )
  
      self.attn_qkvw.data.normal_(
          mean=0.0, std=self.config.initializer_range
      )
      self.attn_qkvb.data.zero_()
      self.attn_ow.data.normal_(mean=0.0, std=output_std)
      self.attn_ob.data.zero_()
      self.attn_nw.data.fill_(1.0)
      self.attn_nb.data.zero_()
      self.inter_w.data.normal_(
          mean=0.0, std=self.config.initializer_range
      )
      self.inter_b.data.zero_()
      self.output_w.data.normal_(mean=0.0, std=output_std)
      self.output_b.data.zero_()
      self.norm_w.data.fill_(1.0)
      self.norm_b.data.zero_()
  
imports: import json
  import math
  import torch
  from torch import nn
  from torch.autograd import Function
  from ..op_builder import TransformerBuilder, StochasticTransformerBuilder
label_desc: Use image node_id 2 for calling the DeepSpeedTransformerLayer obj's underlying member method code with example usage: obj.init_transformer_weights(adjust_init_range) without return types