code: def clip_grad_norm_(
      parameters: _tensor_or_tensors,
      max_norm: float,
      norm_type: float = 2.0,
      error_if_nonfinite: bool = False,
  ) -> torch.Tensor:
      r"""Clips gradient norm of an iterable of parameters.
  
      The norm is computed over all gradients together, as if they were
      concatenated into a single vector. Gradients are modified in-place.
  
      This is identical to torch.nn.utils.clip_grad_norm_, except it
      uses a fused CUDA kernel when computing the 2-norm of GPU tensors
      in float32 and float16.
  
      Args:
          parameters (Iterable[Tensor] or Tensor): an iterable of Tensors or a
              single Tensor that will have gradients normalized
          max_norm (float or int): max norm of the gradients
          norm_type (float or int): type of the used p-norm. Can be ``'inf'`` for
              infinity norm.
          error_if_nonfinite (bool): if True, an error is thrown if the total
              norm of the gradients from :attr:`parameters` is ``nan``,
              ``inf``, or ``-inf``. Default: False (will switch to True in the future)
  
      Returns:
          Total norm of the parameters (viewed as a single vector).
  
      """
      if isinstance(parameters, torch.Tensor):
          parameters = [parameters]
      parameters = [p for p in parameters if p.grad is not None]
      max_norm = float(max_norm)
      norm_type = float(norm_type)
  
      # Trivial case
      if len(parameters) == 0:
          return torch.tensor(0.0)
  
      # Fallback implementation
      if not (
          _kernel_import_succeeded
          and norm_type == 2.0
          and any(p.is_cuda for p in parameters)
      ):
          return torch.nn.utils.clip_grad_norm_(
              parameters,
              max_norm,
              norm_type=norm_type,
              error_if_nonfinite=error_if_nonfinite,
          )
  
      # Find fp32 and fp16 gradients on GPU
      device = next(p.device for p in parameters if p.is_cuda)
      grads_fp32, grads_fp16, grads_misc = [], [], []
      for p in parameters:
          grad = p.grad.detach()
          if p.dtype == torch.float32 and p.device == device:
              grads_fp32.append(grad)
          elif p.dtype == torch.float16 and p.device == device:
              grads_fp16.append(grad)
          else:
              grads_misc.append(grad)
  
      # Compute gradient L2 norms
      norms = []
      dummy_overflow_buf = torch.zeros(
          [1], dtype=torch.int32, device=device
      )
      if grads_fp32:
          norms.append(
              multi_tensor_applier(
                  amp_C.multi_tensor_l2norm,
                  dummy_overflow_buf,
                  [grads_fp32],
                  False,
              )[0]
          )
      if grads_fp16:
          norms.append(
              multi_tensor_applier(
                  amp_C.multi_tensor_l2norm,
                  dummy_overflow_buf,
                  [grads_fp16],
                  False,
              )[0],
          )
      for g in grads_misc:
          norms.append(torch.linalg.norm(g).unsqueeze(0).to(device))
      total_norm = torch.linalg.norm(torch.cat(norms))
  
      # Check for non-finite values
      if error_if_nonfinite and torch.logical_or(
          total_norm.isnan(), total_norm.isinf()
      ):
          raise RuntimeError(
              f"The total norm of order {norm_type} for gradients from "
              "`parameters` is non-finite, so it cannot be clipped. To disable "
              "this error and scale the gradients by the non-finite norm anyway, "
              "set `error_if_nonfinite=False`"
          )
  
      # Scale gradients
      clip_coef = max_norm / (total_norm + 1e-6)
      clip_coef_clamped = torch.clamp(clip_coef, max=1.0)
      if grads_fp32:
          multi_tensor_applier(
              amp_C.multi_tensor_scale,
              dummy_overflow_buf,
              [grads_fp32, grads_fp32],
              clip_coef_clamped,
          )
      if grads_fp16:
          multi_tensor_applier(
              amp_C.multi_tensor_scale,
              dummy_overflow_buf,
              [grads_fp16, grads_fp16],
              clip_coef_clamped,
          )
      for g in grads_misc:
          g.mul_(clip_coef_clamped.to(g.device))
  
      return total_norm
  
label_desc: Use image node_id 1 for calling a global function with example usage: clip_grad_norm_(parameters, max_norm, norm_type, error_if_nonfinite) and returns: total_norm, torch, torch