code: def hamming_window(
      window_length: int,
      /,
      *,
      periodic: bool = True,
      alpha: float = 0.54,
      beta: float = 0.46,
      dtype: Optional[torch.dtype] = None,
      out: Optional[torch.Tensor] = None,
  ) -> torch.Tensor:
      return torch.hamming_window(
          window_length,
          periodic=periodic,
          alpha=alpha,
          beta=beta,
          dtype=dtype,
          layout=torch.strided,
          device=None,
          requires_grad=False,
      )
  
file_path: ivy/ivy/functional/backends/torch/experimental/creation.py
imports: from typing import Optional, Tuple, Union
  import math
  import torch
  import ivy
  from ivy.func_wrapper import with_unsupported_dtypes, with_unsupported_device_and_dtypes
  from ..None import backend_version
label_desc: Use image node_id 2 for calling a global function with example usage: hamming_window() and returns: torch