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__(
      self,
      img_size=224,
      patch_size=16,
      in_chans=3,
      embed_dim=768,
      depth=12,
      num_heads=12,
      mlp_ratio=4,
      qkv_bias=False,
      qk_scale=None,
      drop_rate=0.0,
      attn_drop_rate=0.0,
      drop_path_rate=0.0,
      norm_layer="nn.LayerNorm",
      init_values=None,
      use_rel_pos_bias=False,
      use_shared_rel_pos_bias=False,
      epsilon=1e-5,
      pretrained=None,
      **args,
  ):
      super().__init__()
      self.img_size = img_size
      self.embed_dim = embed_dim
  
      self.patch_embed = PatchEmbed(
          img_size=img_size,
          patch_size=patch_size,
          in_chans=in_chans,
          embed_dim=embed_dim,
      )
      self.pos_w = self.patch_embed.num_patches_in_w
      self.pos_h = self.patch_embed.num_patches_in_h
  
      self.pos_embed = self.create_parameter(
          shape=(1, self.pos_w * self.pos_h + 1, embed_dim),
          default_initializer=paddle.nn.initializer.TruncatedNormal(
              std=0.02
          ),
      )
      self.cls_token = self.create_parameter(
          shape=(1, 1, embed_dim),
          default_initializer=paddle.nn.initializer.Constant(value=0.0),
      )
      self.pos_drop = nn.Dropout(p=drop_rate)
  
      if use_shared_rel_pos_bias:
          self.rel_pos_bias = RelativePositionBias(
              window_size=self.patch_embed.patch_shape,
              num_heads=num_heads,
          )
      else:
          self.rel_pos_bias = None
      self.use_rel_pos_bias = use_rel_pos_bias
  
      dpr = np.linspace(0, drop_path_rate, depth)
  
      self.blocks = nn.LayerList(
          [
              Block(
                  dim=embed_dim,
                  num_heads=num_heads,
                  mlp_ratio=mlp_ratio,
                  qkv_bias=qkv_bias,
                  qk_scale=qk_scale,
                  drop=drop_rate,
                  attn_drop=attn_drop_rate,
                  drop_path=dpr[i],
                  norm_layer=norm_layer,
                  init_values=init_values,
                  window_size=self.patch_embed.patch_shape
                  if use_rel_pos_bias
                  else None,
                  epsilon=epsilon,
              )
              for i in range(depth)
          ]
      )
  
      self.pretrained = pretrained
      self.init_weight()
  
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 1 to create a new CAE object from inherited base classes: nn with example: obj = CAE(img_size, patch_size, in_chans, embed_dim, depth, num_heads, mlp_ratio, qkv_bias, qk_scale, drop_rate, attn_drop_rate, drop_path_rate, norm_layer, init_values, use_rel_pos_bias, use_shared_rel_pos_bias, epsilon, pretrained)