code: def initialize_param(coef_len, **kwargs):
      param_len = coef_len
      method = kwargs["method"]
      fit_intercept = kwargs["fit_intercept"]
      random_state = kwargs.get("random_state", None)
      if fit_intercept:
          param_len = param_len + 1
      if method == "zeros":
          return torch.zeros((param_len, 1), requires_grad=True)
      elif method == "ones":
          return torch.ones((param_len, 1), requires_grad=True)
      elif method == "consts":
          return torch.full(
              (param_len, 1),
              float(kwargs["fill_val"]),
              requires_grad=True,
          )
      elif method == "random":
          if random_state is not None:
              generator = torch.Generator().manual_seed(random_state)
              return torch.randn(
                  (param_len, 1),
                  generator=generator,
                  requires_grad=True,
              )
          return torch.randn((param_len, 1), requires_grad=True)
      elif method == "random_uniform":
          if random_state is not None:
              generator = torch.Generator().manual_seed(random_state)
              return torch.rand(
                  (param_len, 1),
                  generator=generator,
                  requires_grad=True,
              )
          return torch.rand((param_len, 1), requires_grad=True)
      else:
          raise NotImplementedError(
              f"Unknown initialization method: {method}"
          )
  
imports: import torch
label_desc: Use image node_id 2 for calling a global function with example usage: initialize_param(coef_len) and returns: torch, torch, torch, torch, torch, torch, torch