code: def load_weight_from_bin(
      checkpoint_path: PathLike,
      shape: List[int],
      weight_dtype: Union[str, np.dtype],
  ):
      """Load a weight from a bin file.
  
      # Args.
          checkpoint_path: str or Path,
              a checkpoint file path of an FT's layer weight.
          shape: list of int, the shape of weight tensor.
          weight_dtype: str or np.dtype, the data type of the stored weight.
      """
      weight_dtype = to_numpy_dtype(weight_dtype)
      return torch.from_numpy(
          np.fromfile(checkpoint_path, dtype=weight_dtype)
      )
  
imports: import math
  from abc import abstractmethod
  from pathlib import Path
  from typing import List, Literal, Optional, Union
  import os
  import numpy
  from .None import comm
  from .None import profiler
  from .gpt import GptInitModelParameters
  from nebullvm.optional_modules.torch import torch
label_desc: Use image node_id 3 for calling a global function with example usage: load_weight_from_bin(checkpoint_path, shape, weight_dtype) and returns: torch