code: def arange(
      start: float,
      /,
      stop: Optional[float] = None,
      step: float = 1,
      *,
      dtype: Optional[Union[ivy.Dtype, torch.dtype]] = None,
      device: torch.device = None,
      out: Optional[torch.Tensor] = None,
  ) -> torch.Tensor:
      if stop is None:
          stop = start
          start = 0
      if (step > 0 and start > stop) or (step < 0 and start < stop):
          if isinstance(stop, float):
              stop = float(start)
          else:
              stop = start
      if dtype is None:
          if (
              isinstance(start, int)
              and isinstance(stop, int)
              and isinstance(step, int)
          ):
              return torch.arange(
                  start, stop, step, dtype=torch.int64, device=device
              ).to(torch.int32)
          else:
              return torch.arange(start, stop, step, device=device)
      else:
          dtype = ivy.as_native_dtype(ivy.default_dtype(dtype=dtype))
          return torch.arange(
              start, stop, step, dtype=dtype, device=device
          )
  
imports: import copy
  from numbers import Number
  from typing import Union, List, Optional, Sequence, Tuple
  import numpy
  import torch
  from torch import Tensor
  import ivy
  from ivy.func_wrapper import with_unsupported_dtypes, with_unsupported_device_and_dtypes
  from ivy.functional.ivy.creation import _asarray_to_native_arrays_and_back, _asarray_infer_device, _asarray_infer_dtype, _asarray_handle_nestable, NestedSequence, SupportsBufferProtocol, _asarray_inputs_to_native_shapes, _remove_np_bfloat16
  from .None import backend_version
label_desc: Use image node_id 2 for calling a global function with example usage: arange(stop, step) and returns: torch, torch, torch