code: def __init__(
      self,
      d_model,
      nhead,
      dim_feedforward=2048,
      dropout=0.1,
      activation="relu",
      attn_dropout=None,
      act_dropout=None,
      normalize_before=False,
  ):
      super(TransformerEncoderLayer, self).__init__()
      attn_dropout = dropout if attn_dropout is None else attn_dropout
      act_dropout = dropout if act_dropout is None else act_dropout
      self.normalize_before = normalize_before
  
      self.self_attn = MultiHeadAttention(d_model, nhead, attn_dropout)
      # Implementation of Feedforward model
      self.linear1 = nn.Linear(d_model, dim_feedforward)
      self.dropout = nn.Dropout(act_dropout, mode="upscale_in_train")
      self.linear2 = nn.Linear(dim_feedforward, d_model)
  
      self.norm1 = nn.LayerNorm(d_model)
      self.norm2 = nn.LayerNorm(d_model)
      self.dropout1 = nn.Dropout(dropout, mode="upscale_in_train")
      self.dropout2 = nn.Dropout(dropout, mode="upscale_in_train")
      self.activation = getattr(F, activation)
      self._reset_parameters()
  
imports: import math
  import copy
  import numpy
  import paddle
  import paddle.nn
  import paddle.nn.functional
  from ppdet.core.workspace import register, serializable
  from ppdet.modeling.layers import DropBlock, MultiHeadAttention
  from ppdet.modeling.ops import get_act_fn
  from ..backbones.cspresnet import ConvBNLayer, BasicBlock
  from ..shape_spec import ShapeSpec
  from ..initializer import linear_init_
label_desc: Use image node_id 1 to create a new TransformerEncoderLayer object from inherited base classes: nn with example: obj = TransformerEncoderLayer(d_model, nhead, dim_feedforward, dropout, activation, attn_dropout, act_dropout, normalize_before)