code: def __init__(
      self,
      input_element_size: int,
      output_embedding_size: int,
      activation: Optional[nn.Module] = None,
  ):
      super().__init__()
      self.__linear_map = nn.Linear(
          input_element_size, output_embedding_size, bias=False
      )
      torch.nn.init.xavier_uniform(self.__linear_map.weight)
      self.__activation = activation
  
imports: from typing_extensions import Final
  import numpy
  import torch
  from torch import nn
  from typing import Any, Dict, Optional, Union
  from ptgnn.baseneuralmodel import AbstractNeuralModel
  from ptgnn.baseneuralmodel.utils.data import enforce_not_None
  from ptgnn.neuralmodels.gnn.structs import AbstractNodeEmbedder
label_desc: Use image node_id 1 to create a new LinearFeatureEmbedder object from inherited base classes: nn with example: obj = LinearFeatureEmbedder(input_element_size, output_embedding_size, activation)