class_docstr: The EABlock implementation based on PaddlePaddle.
  Args:
      in_channels (int, optional): The input channels.
      out_channels (int, optional): The output channels.
      num_heads (int, optional): The num of heads in attention. Default: 8
      drop_rate (float, optional): The drop rate in MLP. Default:0.
      drop_path_rate (float, optional): The drop path rate in EABlock. Default: 0.2
      use_injection (bool, optional): Whether inject the high feature into low feature. Default: True
      use_cross_kv (bool, optional): Wheter use cross_kv. Default: True
      cross_size (int, optional): The size of pooling in cross_kv. Default: 12
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code: def _init_weights_kaiming(self, m):
      if isinstance(m, nn.Linear):
          trunc_normal_init(m.weight, std=0.02)
          if m.bias is not None:
              constant_init(m.bias, value=0)
      elif isinstance(m, (nn.SyncBatchNorm, nn.BatchNorm2D)):
          constant_init(m.weight, value=1.0)
          constant_init(m.bias, value=0)
      elif isinstance(m, nn.Conv2D):
          kaiming_normal_init(m.weight)
          if m.bias is not None:
              constant_init(m.bias, value=0)
  
imports: from functools import partial
  import paddle
  import paddle.nn
  import paddle.nn.functional
  from paddleseg.cvlibs import manager, param_init
  from paddleseg.models import layers
  from paddleseg.utils import utils
  from paddleseg.models.backbones.transformer_utils import DropPath, Identity
  from paddleseg.cvlibs.param_init import constant_init, kaiming_normal_init, trunc_normal_init
label_desc: Use image node_id 3 for calling the EABlock obj's underlying member method code with example usage: obj._init_weights_kaiming(m) without return types