class_docstr: The Context Autoencoder for Self-Supervised Representation Learning implemetation based on PaddlePaddle
  
  The original article refers to Chen, Xiaokang, Mingyu Ding, Xiaodi Wang, Ying Xin, Shentong Mo, Yunhao Wang, Shumin Han, Ping Luo, Gang Zeng, and Jingdong Wang. "Context autoencoder for self-supervised representation learning." arXiv preprint arXiv:2202.03026 (2022).
  (https://arxiv.org/abs/2202.03026)
  
  Args:
      img_size (int): Input image size for training the pretrained model, used in absolute postion embedding. Default: 224.
      patch_size (int | tuple(int)): Patch size. Default: 4.
      in_chans (int): Number of input image channels. Default: 3.
      embed_dim (int): Number of linear projection output channels. Default: 96.
      depths (tuple[int]): Depths of each Swin Transformer stage.
      num_heads (tuple[int]): Number of attention head of each stage.
      window_size (int): Window size. Default: 7.
      mlp_ratio (float): Ratio of mlp hidden dim to embedding dim. Default: 4.
      qkv_bias (bool): If True, add a learnable bias to query, key, value. Default: True
      qk_scale (float): Override default qk scale of head_dim ** -0.5 if set.
      drop_rate (float): Dropout rate. Default: 0.
      attn_drop_rate (float): Attention dropout rate. Default: 0.
      drop_path_rate (float): Stochastic depth rate. Default: 0.0.
      norm_layer (nn.Layer): Normalization layer. Default: nn.LayerNorm.
      init_values(float): The initial value of dropout in the block. Default: None.
      use_rel_pos_bias(bool): Whether to use relative position bias. Default: False.
      use_shared_rel_pos_bias(bool): Whether to use relative position bias. Default: False.
      epsilon(float): Epsilon in first norm of block. Default: 1e-5.
      pretrained (str, optional): The path or url of pretrained model. Default: None.
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code: def init_weight(self):
      if self.pretrained:
          utils.load_pretrained_model(self, self.pretrained)
  
      model_path = self.pretrained
      if not os.path.exists(model_path):
          model_path = utils.download_pretrained_model(model_path)
  
      load_state_dict = paddle.load(model_path)
      model_state_dict = self.state_dict()
      pos_embed_name = "pos_embed"
      if pos_embed_name in load_state_dict.keys():
          load_pos_embed = paddle.to_tensor(
              load_state_dict[pos_embed_name], dtype="float32"
          )
          if self.pos_embed.shape != load_pos_embed.shape:
              pos_size = int(math.sqrt(load_pos_embed.shape[1] - 1))
              model_state_dict[pos_embed_name] = self.resize_pos_embed(
                  load_pos_embed,
                  (pos_size, pos_size),
                  (self.pos_h, self.pos_w),
              )
              self.set_dict(model_state_dict)
              logger.info(
                  "Load pos_embed and resize it from {} to {} .".format(
                      load_pos_embed.shape, self.pos_embed.shape
                  )
              )
  
imports: import os
  import math
  import numpy
  import paddle
  import paddle.nn
  import paddle.nn.functional
  from paddle.nn.initializer import TruncatedNormal, Constant, Normal
  from paddleseg.cvlibs import manager
  from paddleseg.utils import utils, logger
  from paddleseg.models.backbones.transformer_utils import to_2tuple, DropPath, Identity
label_desc: Use image node_id 2 for calling the CAE obj's underlying member method code with example usage: obj.init_weight() without return types