code: def uniform_init_parameters(module):
      """Initialize parameters with an uniform distribution."""
      for p in module.parameters():
          data = p.data
          if data.dim() == 1:
              # bias
              data.uniform_(-0.1, 0.1)
          elif data.dim() == 2:
              # linear weight
              data.uniform_(-0.1, 0.1)
          elif data.dim() in (3, 4):
              # conv weight
              pass  # use the pytorch default
          else:
              raise NotImplementedError
  
label_desc: Use image node_id 2 for calling a global function with example usage: uniform_init_parameters(module) without return types