code: def __init__(
      self,
      units=512,
      hidden_size=2048,
      num_layers=6,
      num_groups=1,
      num_heads=8,
      attention_dropout_prob=0.0,
      hidden_dropout_prob=0.0,
      output_attention=False,
      dtype="float32",
      output_all_encodings=False,
      layer_norm_eps=1e-12,
      weight_initializer=TruncNorm(stdev=0.02),
      bias_initializer="zeros",
      activation="gelu",
      layout="NT",
  ):
      super().__init__()
      assert units % num_heads == 0, (
          "In AlbertEncoder, The units should be divided exactly "
          "by the number of heads. Received units={}, num_heads={}".format(
              units, num_heads
          )
      )
  
      self._dtype = dtype
      self._num_layers = num_layers
      self._num_groups = num_groups
      assert num_layers % num_groups == 0
      self._num_layers_each_group = num_layers // num_groups
  
      self._output_attention = output_attention
      self._output_all_encodings = output_all_encodings
      self._layout = layout
  
      self.all_encoder_groups = nn.HybridSequential()
      for group_idx in range(num_groups):
          self.all_encoder_groups.add(
              TransformerEncoderLayer(
                  units=units,
                  hidden_size=hidden_size,
                  num_heads=num_heads,
                  attention_dropout_prob=attention_dropout_prob,
                  hidden_dropout_prob=hidden_dropout_prob,
                  layer_norm_eps=layer_norm_eps,
                  weight_initializer=weight_initializer,
                  bias_initializer=bias_initializer,
                  activation=activation,
                  dtype=dtype,
                  layout=layout,
              )
          )
  
imports: import os
  from typing import Tuple
  import mxnet
  from mxnet import use_np
  from mxnet import np, npx
  from mxnet.gluon import HybridBlock, nn
  from .transformer import TransformerEncoderLayer
  from .base import BACKBONE_REGISTRY
  from ..base import get_model_zoo_home_dir, get_repo_model_zoo_url, get_model_zoo_checksum_dir
  from ..utils.config import CfgNode
  from ..utils.misc import load_checksum_stats, download
  from ..utils.registry import Registry
  from ..initializer import TruncNorm
  from ..attention_cell import gen_self_attn_mask
  from ..layers import get_activation, PositionalEmbedding
  from ..op import select_vectors_by_position
  from ..data.tokenizers import SentencepieceTokenizer
label_desc: Use image node_id 1 to create a new AlbertEncoder object from inherited base classes: HybridBlock with example: obj = AlbertEncoder(units, hidden_size, num_layers, num_groups, num_heads, attention_dropout_prob, hidden_dropout_prob, output_attention, dtype, output_all_encodings, layer_norm_eps, weight_initializer, bias_initializer, activation, layout)