class_docstr: Standard heads for FPN-based models
  
  Args:
      in_channels (int): number of input channels
      representation_size (int): size of the intermediate representation
class_docstr_tok: ["Standard","heads","for","FPN-based","models","Args",":","in_channels","(","int",")",":","number","of","input","channels","representation_size","(","int",")",":","size","of","the","intermediate","representation"]
code: def forward(self, x):
      x = x.flatten(start_dim=1)
  
      x = F.relu(self.fc6(x))
      x = F.relu(self.fc7(x))
  
      return x
  
imports: from typing import Any, Callable, List, Optional, Tuple, Union
  import torch
  import torch.nn.functional
  from torch import nn
  from torchvision.ops import MultiScaleRoIAlign
  from ...ops import misc
  from ...transforms._presets import ObjectDetection
  from .._api import register_model, Weights, WeightsEnum
  from .._meta import _COCO_CATEGORIES
  from .._utils import _ovewrite_value_param, handle_legacy_interface
  from ..mobilenetv3 import mobilenet_v3_large, MobileNet_V3_Large_Weights
  from ..resnet import resnet50, ResNet50_Weights
  from ._utils import overwrite_eps
  from .anchor_utils import AnchorGenerator
  from .backbone_utils import _mobilenet_extractor, _resnet_fpn_extractor, _validate_trainable_layers
  from .generalized_rcnn import GeneralizedRCNN
  from .roi_heads import RoIHeads
  from .rpn import RegionProposalNetwork, RPNHead
  from .transform import GeneralizedRCNNTransform
label_desc: Use image node_id 2 for calling the TwoMLPHead obj's underlying member method code with example usage: obj.forward(x) and returns: x