class_docstr: A general wrapper for layers.
  
  Args:
      name (str): The registered name of the layer.
      layer_config (LayerConfig): The configuration of the layer.
      **kwargs (optional): Additional keyword arguments.
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code: def __init__(self, name, layer_config: LayerConfig, **kwargs):
      super().__init__()
      self.has_l2norm = layer_config.has_l2norm
      has_bn = layer_config.has_batchnorm
      layer_config.has_bias = not has_bn
      self.layer = register.layer_dict[name](layer_config, **kwargs)
      layer_wrapper = []
      if has_bn:
          layer_wrapper.append(
              torch.nn.BatchNorm1d(
                  layer_config.dim_out,
                  eps=layer_config.bn_eps,
                  momentum=layer_config.bn_mom,
              )
          )
      if layer_config.dropout > 0:
          layer_wrapper.append(
              torch.nn.Dropout(
                  p=layer_config.dropout,
                  inplace=layer_config.mem_inplace,
              )
          )
      if layer_config.has_act:
          layer_wrapper.append(register.act_dict[layer_config.act]())
      self.post_layer = torch.nn.Sequential(*layer_wrapper)
  
imports: import copy
  from dataclasses import dataclass, replace
  import torch
  import torch.nn.functional
  import torch_geometric
  import torch_geometric.graphgym.models.act
  import torch_geometric.graphgym.register
  from torch_geometric.graphgym.contrib.layer.generalconv import GeneralConvLayer, GeneralEdgeConvLayer
  from torch_geometric.graphgym.register import register_layer
  from torch_geometric.nn import Linear
label_desc: Use image node_id 1 to create a new GeneralLayer object from inherited base classes: torch.nn with example: obj = GeneralLayer(name, layer_config)