code: def reset_weight_(
      weight: Tensor,
      in_channels: int,
      initializer: Optional[str] = None,
  ) -> Tensor:
      if in_channels <= 0:
          pass
      elif initializer == "glorot":
          inits.glorot(weight)
      elif initializer == "uniform":
          bound = 1.0 / math.sqrt(in_channels)
          torch.nn.init.uniform_(weight.data, -bound, bound)
      elif initializer == "kaiming_uniform":
          inits.kaiming_uniform(weight, fan=in_channels, a=math.sqrt(5))
      elif initializer is None:
          inits.kaiming_uniform(weight, fan=in_channels, a=math.sqrt(5))
      else:
          raise RuntimeError(
              f"Weight initializer '{initializer}' not supported"
          )
  
      return weight
  
imports: import copy
  import math
  from typing import Any, Dict, Optional, Union
  import torch
  import torch.nn.functional
  from torch import Tensor
  from torch.nn.parameter import Parameter
  import torch_geometric.backend
  import torch_geometric.typing
  from torch_geometric import is_compiling
  from torch_geometric.nn import inits
  from torch_geometric.typing import pyg_lib
  from torch_geometric.utils import index_sort, scatter
  from torch_geometric.utils.sparse import index2ptr
label_desc: Use image node_id 2 for calling a global function with example usage: reset_weight_(weight, in_channels, initializer) and returns: weight