class_docstr: Encodes node and edge features, given the specified input dimension and
  the underlying configuration in :obj:`cfg`.
  
  Args:
      dim_in (int): The input feature dimension.
class_docstr_tok: ["Encodes","node","and","edge","features",",","given","the","specified","input","dimension","and","the","underlying","configuration","in",":","obj",":","`","cfg","`",".","Args",":","dim_in","(","int",")",":","The","input","feature","dimension","."]
code: def forward(self, batch):
      for module in self.children():
          batch = module(batch)
      return batch
  
imports: import torch
  import torch.nn.functional
  import torch_geometric.graphgym.register
  from torch_geometric.graphgym.config import cfg
  from torch_geometric.graphgym.init import init_weights
  from torch_geometric.graphgym.models.layer import BatchNorm1dNode, GeneralLayer, GeneralMultiLayer, new_layer_config
  from torch_geometric.graphgym.register import register_stage
label_desc: Use image node_id 2 for calling the FeatureEncoder obj's underlying member method code with example usage: obj.forward(batch) and returns: batch