class_docstr: Implements one encoder layer in DETR transformer.
  
  Args:
      embed_dims (int): The feature dimension. Same as `FFN`.
      num_heads (int): Parallel attention heads.
      feedforward_channels (int): The hidden dimension for FFNs.
      dropout (float): Probability of an element to be zeroed. Default 0.0.
      order (tuple[str]): The order for encoder layer. Valid examples are
          ('selfattn', 'norm', 'ffn', 'norm') and ('norm', 'selfattn',
          'norm', 'ffn'). Default ('selfattn', 'norm', 'ffn', 'norm').
      act_cfg (dict): The activation config for FFNs. Defalut ReLU.
      norm_cfg (dict): Config dict for normalization layer. Default
          layer normalization.
      num_fcs (int): The number of fully-connected layers for FFNs.
          Default 2.
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docstr: str: a string that describes the module
docstr_tok: ["str",":","a","string","that","describes","the","module"]
code: def __repr__(self):
      """str: a string that describes the module"""
      repr_str = self.__class__.__name__
      repr_str += f"(embed_dims={self.embed_dims}, "
      repr_str += f"num_heads={self.num_heads}, "
      repr_str += f"feedforward_channels={self.feedforward_channels}, "
      repr_str += f"dropout={self.dropout}, "
      repr_str += f"order={self.order}, "
      repr_str += f"act_cfg={self.act_cfg}, "
      repr_str += f"norm_cfg={self.norm_cfg}, "
      repr_str += f"num_fcs={self.num_fcs})"
      return repr_str
  
imports: import torch
  import torch.nn
  from mmcv.cnn import Linear, build_activation_layer, build_norm_layer, xavier_init
  from .builder import TRANSFORMER
label_desc: Use image node_id 3 for calling the TransformerEncoderLayer obj's underlying member method code with example usage: obj.__repr__() and returns: repr_str