docstr: A simple conversion of the BERT layer to its `BetterTransformer` implementation.
  
  Args:
      bert_layer (`torch.nn.Module`):
          The original BERT Layer where the weights needs to be retrieved.
code: def __init__(self, bert_layer, config):
      r"""
      A simple conversion of the BERT layer to its `BetterTransformer` implementation.
  
      Args:
          bert_layer (`torch.nn.Module`):
              The original BERT Layer where the weights needs to be retrieved.
      """
      super().__init__(config)
      super(BetterTransformerBaseLayer, self).__init__()
      # In_proj layer
      self.in_proj_weight = nn.Parameter(
          torch.cat(
              [
                  bert_layer.attention.self.query.weight,
                  bert_layer.attention.self.key.weight,
                  bert_layer.attention.self.value.weight,
              ]
          )
      )
      self.in_proj_bias = nn.Parameter(
          torch.cat(
              [
                  bert_layer.attention.self.query.bias,
                  bert_layer.attention.self.key.bias,
                  bert_layer.attention.self.value.bias,
              ]
          )
      )
  
      # Out proj layer
      self.out_proj_weight = bert_layer.attention.output.dense.weight
      self.out_proj_bias = bert_layer.attention.output.dense.bias
  
      # Linear layer 1
      self.linear1_weight = bert_layer.intermediate.dense.weight
      self.linear1_bias = bert_layer.intermediate.dense.bias
  
      # Linear layer 2
      self.linear2_weight = bert_layer.output.dense.weight
      self.linear2_bias = bert_layer.output.dense.bias
  
      # Layer norm 1
      self.norm1_eps = bert_layer.attention.output.LayerNorm.eps
      self.norm1_weight = bert_layer.attention.output.LayerNorm.weight
      self.norm1_bias = bert_layer.attention.output.LayerNorm.bias
  
      # Layer norm 2
      self.norm2_eps = bert_layer.output.LayerNorm.eps
      self.norm2_weight = bert_layer.output.LayerNorm.weight
      self.norm2_bias = bert_layer.output.LayerNorm.bias
  
      # Model hyper parameters
      self.num_heads = bert_layer.attention.self.num_attention_heads
      self.embed_dim = bert_layer.attention.self.all_head_size
  
      # Last step: set the last layer to `False` -> this will be set to `True` when converting the model
      self.is_last_layer = False
  
      self.original_layers_mapping = {
          "in_proj_weight": [
              "attention.self.query.weight",
              "attention.self.key.weight",
              "attention.self.value.weight",
          ],
          "in_proj_bias": [
              "attention.self.query.bias",
              "attention.self.key.bias",
              "attention.self.value.bias",
          ],
          "out_proj_weight": "attention.output.dense.weight",
          "out_proj_bias": "attention.output.dense.bias",
          "linear1_weight": "intermediate.dense.weight",
          "linear1_bias": "intermediate.dense.bias",
          "linear2_weight": "output.dense.weight",
          "linear2_bias": "output.dense.bias",
          "norm1_eps": "attention.output.LayerNorm.eps",
          "norm1_weight": "attention.output.LayerNorm.weight",
          "norm1_bias": "attention.output.LayerNorm.bias",
          "norm2_eps": "output.LayerNorm.eps",
          "norm2_weight": "output.LayerNorm.weight",
          "norm2_bias": "output.LayerNorm.bias",
      }
      self.attention_head_size = int(
          config.hidden_size / config.num_attention_heads
      )
      self.hidden_dropout_prob = config.hidden_dropout_prob
      self.attention_probs_dropout_prob = (
          config.attention_probs_dropout_prob
      )
      self.act_fn_callable = ACT2FN[self.act_fn]
  
      self.validate_bettertransformer()
  
imports: from typing import TYPE_CHECKING
  import torch
  import torch.nn
  import torch.nn.functional
  from transformers.activations import ACT2FN
  from .base import BetterTransformerBaseLayer
label_desc: Use image node_id 1 to create a new BertLayerBetterTransformer object from inherited base classes: BetterTransformerBaseLayer, nn with example: obj = BertLayerBetterTransformer(bert_layer, config)