docstr: A simple conversion of the ALBERT layer to its `BetterTransformer` implementation.
  
  Args:
      albert_layer (`torch.nn.Module`):
          The original ALBERT Layer where the weights needs to be retrieved.
code: def __init__(self, albert_layer, config):
      r"""
      A simple conversion of the ALBERT layer to its `BetterTransformer` implementation.
  
      Args:
          albert_layer (`torch.nn.Module`):
              The original ALBERT 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(
              [
                  albert_layer.attention.query.weight,
                  albert_layer.attention.key.weight,
                  albert_layer.attention.value.weight,
              ]
          )
      )
      self.in_proj_bias = nn.Parameter(
          torch.cat(
              [
                  albert_layer.attention.query.bias,
                  albert_layer.attention.key.bias,
                  albert_layer.attention.value.bias,
              ]
          )
      )
  
      # Out proj layer
      self.out_proj_weight = albert_layer.attention.dense.weight
      self.out_proj_bias = albert_layer.attention.dense.bias
  
      # Linear layer 1
      self.linear1_weight = albert_layer.ffn.weight
      self.linear1_bias = albert_layer.ffn.bias
  
      # Linear layer 2
      self.linear2_weight = albert_layer.ffn_output.weight
      self.linear2_bias = albert_layer.ffn_output.bias
  
      # Layer norm 1
      self.norm1_eps = albert_layer.attention.LayerNorm.eps
      self.norm1_weight = albert_layer.attention.LayerNorm.weight
      self.norm1_bias = albert_layer.attention.LayerNorm.bias
  
      # Layer norm 2
      self.norm2_eps = albert_layer.full_layer_layer_norm.eps
      self.norm2_weight = albert_layer.full_layer_layer_norm.weight
      self.norm2_bias = albert_layer.full_layer_layer_norm.bias
  
      # Model hyper parameters
      self.num_heads = albert_layer.attention.num_attention_heads
      self.embed_dim = albert_layer.attention.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.query.weight",
              "attention.key.weight",
              "attention.value.weight",
          ],
          "in_proj_bias": [
              "attention.query.bias",
              "attention.key.bias",
              "attention.value.bias",
          ],
          "out_proj_weight": "attention.dense.weight",
          "out_proj_bias": "attention.dense.bias",
          "linear1_weight": "ffn.weight",
          "linear1_bias": "ffn.bias",
          "linear2_weight": "ffn_output.weight",
          "linear2_bias": "ffn_output.bias",
          "norm1_eps": "attention.LayerNorm.eps",
          "norm1_weight": "attention.LayerNorm.weight",
          "norm1_bias": "attention.LayerNorm.bias",
          "norm2_eps": "full_layer_layer_norm.eps",
          "norm2_weight": "full_layer_layer_norm.weight",
          "norm2_bias": "full_layer_layer_norm.bias",
      }
      self.attention_head_size = (
          config.hidden_size // config.num_attention_heads
      )
      self.attention_probs_dropout_prob = (
          config.attention_probs_dropout_prob
      )
      self.hidden_dropout_prob = config.hidden_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 AlbertLayerBetterTransformer object from inherited base classes: BetterTransformerBaseLayer, nn with example: obj = AlbertLayerBetterTransformer(albert_layer, config)