code: def float_power(
      x1: Union[torch.Tensor, float, list, tuple],
      x2: Union[torch.Tensor, float, list, tuple],
      /,
      *,
      out: Optional[torch.Tensor] = None,
  ) -> torch.Tensor:
      # Native out is supported but with restrictions leading
      # to failures hence letting ivy handle it.
      x1, x2 = promote_types_of_inputs(x1, x2)
      return torch.float_power(x1, x2, out=out)
  
imports: from typing import Optional, Union, Tuple, List, Sequence
  from numbers import Number
  import torch
  import ivy
  from ivy import promote_types_of_inputs
  from ivy.functional.backends.torch.elementwise import _cast_for_unary_op
  from ivy.func_wrapper import with_unsupported_dtypes, with_supported_dtypes
  from ..None import backend_version
label_desc: Use image node_id 6 for calling a global function with example usage: float_power() and returns: torch