code: def __init__(self, in_channels):
      super(Cross_Attention, self).__init__()
      self.theta_conv = ConvBNLayer(
          in_channels, in_channels, 1, 1, act="relu", name="f_theta"
      )
      self.phi_conv = ConvBNLayer(
          in_channels, in_channels, 1, 1, act="relu", name="f_phi"
      )
      self.g_conv = ConvBNLayer(
          in_channels, in_channels, 1, 1, act="relu", name="f_g"
      )
  
      self.fh_weight_conv = ConvBNLayer(
          in_channels, in_channels, 1, 1, act=None, name="fh_weight"
      )
      self.fh_sc_conv = ConvBNLayer(
          in_channels, in_channels, 1, 1, act=None, name="fh_sc"
      )
  
      self.fv_weight_conv = ConvBNLayer(
          in_channels, in_channels, 1, 1, act=None, name="fv_weight"
      )
      self.fv_sc_conv = ConvBNLayer(
          in_channels, in_channels, 1, 1, act=None, name="fv_sc"
      )
  
      self.f_attn_conv = ConvBNLayer(
          in_channels * 2, in_channels, 1, 1, act="relu", name="f_attn"
      )
  
imports: from __future__ import absolute_import
  from __future__ import division
  from __future__ import print_function
  import paddle
  from paddle import nn
  import paddle.nn.functional
  from paddle import ParamAttr
label_desc: Use image node_id 1 to create a new Cross_Attention object from inherited base classes: nn with example: obj = Cross_Attention(in_channels)