code: def set_module_8bit_tensor_to_device(
      module, tensor_name, device, value=None
  ):
      """
      A helper function to set a given tensor (parameter of buffer) of a module on a specific device (note that doing
      `param.to(device)` creates a new tensor not linked to the parameter, which is why we need this function). The
      function is adapted from `set_module_tensor_to_device` function from accelerate that is adapted to support the
      class `Int8Params` from `bitsandbytes`.
  
      Args:
          module (`torch.nn.Module`):
              The module in which the tensor we want to move lives.
          tensor_name (`str`):
              The full name of the parameter/buffer.
          device (`int`, `str` or `torch.device`):
              The device on which to set the tensor.
          value (`torch.Tensor`, *optional*):
              The value of the tensor (useful when going from the meta device to any other device).
      """
      # Recurse if needed
      if "." in tensor_name:
          splits = tensor_name.split(".")
          for split in splits[:-1]:
              new_module = getattr(module, split)
              if new_module is None:
                  raise ValueError(
                      f"{module} has no attribute {split}."
                  )
              module = new_module
          tensor_name = splits[-1]
  
      if (
          tensor_name not in module._parameters
          and tensor_name not in module._buffers
      ):
          raise ValueError(
              f"{module} does not have a parameter or a buffer named {tensor_name}."
          )
      is_buffer = tensor_name in module._buffers
      old_value = getattr(module, tensor_name)
  
      if (
          old_value.device == torch.device("meta")
          and device not in ["meta", torch.device("meta")]
          and value is None
      ):
          raise ValueError(
              f"{tensor_name} is on the meta device, we need a `value` to put in on {device}."
          )
  
      if is_buffer:
          has_fp16_weights = None
      else:
          has_fp16_weights = getattr(
              module._parameters[tensor_name], "has_fp16_weights", None
          )
  
      if has_fp16_weights is not None:
          param = module._parameters[tensor_name]
          if param.device.type != "cuda":
              if value is None:
                  new_value = old_value.to(device)
              elif isinstance(value, torch.Tensor):
                  new_value = value.to("cpu")
                  if value.dtype == torch.int8:
                      raise ValueError(
                          "You cannot load weights that are saved in int8 using `load_in_8bit=True`, make sure you are",
                          " using `load_in_8bit=True` on float32/float16/bfloat16 weights.",
                      )
              else:
                  new_value = torch.tensor(value, device="cpu")
              new_value = bnb.nn.Int8Params(
                  new_value,
                  requires_grad=False,
                  has_fp16_weights=has_fp16_weights,
              ).to(device)
              module._parameters[tensor_name] = new_value
      else:
          if value is None:
              new_value = old_value.to(device)
          elif isinstance(value, torch.Tensor):
              new_value = value.to(device)
          else:
              new_value = torch.tensor(value, device=device)
  
          if is_buffer:
              module._buffers[tensor_name] = new_value
          else:
              new_value = nn.Parameter(
                  new_value, requires_grad=old_value.requires_grad
              )
              module._parameters[tensor_name] = new_value
  
imports: from copy import deepcopy
  from transformers.utils import is_accelerate_available, is_bitsandbytes_available
label_desc: Use image node_id 1 for calling a global function with example usage: set_module_8bit_tensor_to_device(module, tensor_name, device, value) without return types