code: def __init__(
      self,
      units=768,
      hidden_size=3072,
      num_layers=12,
      num_heads=12,
      attention_dropout_prob=0.1,
      hidden_dropout_prob=0.1,
      layer_norm_eps=1e-5,
      weight_initializer=TruncNorm(stdev=0.02),
      bias_initializer="zeros",
      activation="gelu",
      dtype="float32",
      output_all_encodings=False,
      output_attention=False,
      layout="NT",
  ):
      super().__init__()
      self.units = units
      self.hidden_size = hidden_size
      self.num_layers = num_layers
      self.num_heads = num_heads
      self.attention_dropout_prob = attention_dropout_prob
      self.hidden_dropout_prob = hidden_dropout_prob
      self.layer_norm_eps = layer_norm_eps
      self.activation = activation
      self._dtype = dtype
      self._layout = layout
      self._output_all_encodings = output_all_encodings
      self._output_attention = output_attention
      self.all_layers = nn.HybridSequential()
      for layer_idx in range(self.num_layers):
          self.all_layers.add(
              TransformerEncoderLayer(
                  units=self.units,
                  hidden_size=self.hidden_size,
                  num_heads=self.num_heads,
                  attention_dropout_prob=self.attention_dropout_prob,
                  hidden_dropout_prob=self.hidden_dropout_prob,
                  layer_norm_eps=self.layer_norm_eps,
                  weight_initializer=weight_initializer,
                  bias_initializer=bias_initializer,
                  activation=self.activation,
                  dtype=self._dtype,
                  layout=layout,
              )
          )
  
imports: import os
  from typing import Tuple
  import mxnet
  from mxnet import use_np, np, npx
  from mxnet.gluon import HybridBlock, nn
  from .base import BACKBONE_REGISTRY
  from ..op import select_vectors_by_position
  from ..base import get_model_zoo_home_dir, get_repo_model_zoo_url, get_model_zoo_checksum_dir
  from ..layers import PositionalEmbedding, get_activation
  from ..utils.misc import download, load_checksum_stats
  from ..utils.registry import Registry
  from .transformer import TransformerEncoderLayer
  from ..initializer import TruncNorm
  from ..utils.config import CfgNode
  from ..attention_cell import gen_self_attn_mask
  from ..data.tokenizers import HuggingFaceByteBPETokenizer
label_desc: Use image node_id 1 to create a new RobertaEncoder object from inherited base classes: HybridBlock with example: obj = RobertaEncoder(units, hidden_size, num_layers, num_heads, attention_dropout_prob, hidden_dropout_prob, layer_norm_eps, weight_initializer, bias_initializer, activation, dtype, output_all_encodings, output_attention, layout)