code: def asarray(
      obj: Union[
          torch.Tensor,
          np.ndarray,
          torch.Size,
          bool,
          int,
          float,
          NestedSequence,
          SupportsBufferProtocol,
      ],
      /,
      *,
      copy: Optional[bool] = None,
      dtype: Optional[Union[ivy.Dtype, torch.dtype]] = None,
      device: torch.device = None,
      out: Optional[torch.Tensor] = None,
  ) -> torch.Tensor:
      obj = ivy.nested_map(_remove_np_bfloat16, obj, shallow=False)
      if isinstance(obj, Sequence) and len(obj) != 0:
          contain_tensor = ivy.nested_any(
              obj, lambda x: isinstance(x, torch.Tensor)
          )
          # if `obj` is a list of specifically tensors or
          # a multidimensional list which contains a tensor
          if contain_tensor:
              ret = _stack_tensors(obj, dtype).to(device)
              return ret.clone().detach() if copy else ret
      try:
          ret = torch.as_tensor(obj, dtype=dtype, device=device)
      except ValueError as e:
          if (
              "At least one stride in the given numpy array is negative"
              in str(e)
          ):
              ret = torch.as_tensor(
                  obj.copy(), dtype=dtype, device=device
              )
          else:
              raise
      return ret.clone().detach() if copy else ret
  
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 4 for calling a global function with example usage: asarray() and returns: unknown, unknown