class_docstr: RCNN bbox head with Two fc layers to extract feature
  
  Args:
      in_channel (int): Input channel which can be derived by from_config
      out_channel (int): Output channel
      resolution (int): Resolution of input feature map, default 7
class_docstr_tok: ["RCNN","bbox","head","with","Two","fc","layers","to","extract","feature","Args",":","in_channel","(","int",")",":","Input","channel","which","can","be","derived","by","from_config","out_channel","(","int",")",":","Output","channel","resolution","(","int",")",":","Resolution","of","input","feature","map",",","default","7"]
code: def forward(self, rois_feat):
      rois_feat = paddle.flatten(rois_feat, start_axis=1, stop_axis=-1)
      fc6 = self.fc6(rois_feat)
      fc6 = F.relu(fc6)
      fc7 = self.fc7(fc6)
      fc7 = F.relu(fc7)
      return fc7
  
imports: import numpy
  import paddle
  import paddle.nn
  import paddle.nn.functional
  from paddle.nn.initializer import Normal, XavierUniform, KaimingNormal
  from paddle.regularizer import L2Decay
  from ppdet.core.workspace import register, create
  from .roi_extractor import RoIAlign
  from ..shape_spec import ShapeSpec
  from ..bbox_utils import bbox2delta
  from ..cls_utils import _get_class_default_kwargs
  from ppdet.modeling.layers import ConvNormLayer
label_desc: Use image node_id 4 for calling the TwoFCHead obj's underlying member method code with example usage: obj.forward(rois_feat) and returns: fc7