code: def asarray(
      obj: Union[
          paddle.Tensor,
          np.ndarray,
          bool,
          int,
          float,
          list,
          NestedSequence,
          SupportsBufferProtocol,
      ],
      /,
      *,
      copy: Optional[bool] = None,
      dtype: Optional[Union[ivy.Dtype, paddle.dtype]] = None,
      device: core.Place = None,
      out: Optional[paddle.Tensor] = None,
  ) -> paddle.Tensor:
      if isinstance(obj, paddle.Tensor):
          if copy:
              # Checking if the tensor is not empty
              # As clone is not supported for empty tensors
              if all(obj.shape):
                  ret = obj.clone().detach()
                  ret.stop_gradient = obj.stop_gradient
              else:
                  ret = paddle.to_tensor(
                      obj.detach(),
                      dtype=dtype,
                      place=device,
                      stop_gradient=obj.stop_gradient,
                  )
          else:
              ret = obj
          ret = ret.astype(dtype) if ret.dtype != obj.dtype else ret
          return paddle_backend.to_device(ret, device)
  
      elif isinstance(obj, (Number, bool, complex)):
          ret = paddle.to_tensor(obj, dtype=dtype, place=device)
  
          if ret.ndim != 0:  # for versions <2.5.0
              return ret.squeeze()
          else:
              return ret
      obj = ivy.nested_map(_remove_np_bfloat16, obj, shallow=False)
      return paddle.to_tensor(obj, dtype=dtype, place=device)
  
imports: import struct
  from numbers import Number
  from typing import Union, List, Optional, Sequence, Tuple
  import numpy
  import paddle
  import ivy.functional.backends.paddle
  import ivy
  from ivy.func_wrapper import with_unsupported_device_and_dtypes
  from ivy.functional.ivy.creation import _asarray_to_native_arrays_and_back, _asarray_infer_device, _asarray_handle_nestable, _asarray_infer_dtype, NestedSequence, SupportsBufferProtocol, _asarray_inputs_to_native_shapes, _remove_np_bfloat16
  from .None import backend_version
  from paddle.device import core
label_desc: Use image node_id 2 for calling a global function with example usage: asarray() and returns: paddle, paddle_backend, ret, ret