code: def l1_normalize(
      x: paddle.Tensor,
      /,
      *,
      axis: Optional[int] = None,
      out: paddle.Tensor = None,
  ) -> paddle.Tensor:
      if not isinstance(x, paddle.Tensor):
          x = paddle.to_tensor(x)
      if axis is None:
          axis = list(range(x.ndim))
      elif isinstance(axis, int):
          axis = [axis]
      else:
          axis = list(axis)
  
      # Compute the L1 norm along the given axis
      norm = paddle.norm(x, p=1, axis=axis, keepdim=True)
  
      # Divide x by the L1 norm to obtain the normalized array
      norm = paddle.where(
          norm == 0, paddle.to_tensor([1], dtype=x.dtype), norm
      )
      if out is None:
          return x / norm
      else:
          out[:] = x / norm
          return out
  
file_path: ivy/ivy/functional/backends/paddle/experimental/norms.py
imports: import paddle
  import paddle.nn.functional
  import ivy
  from ivy.utils.exceptions import IvyNotImplementedException
  from typing import Optional, Tuple
  from ivy.func_wrapper import with_supported_dtypes
  from ivy.func_wrapper import 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: l1_normalize() and returns: unknown, out