code: def broadcast_arrays(*arrays: paddle.Tensor) -> List[paddle.Tensor]:
      if len(arrays) > 1:
          desired_shape = paddle_backend.broadcast_shapes(
              arrays[0].shape, arrays[1].shape
          )
          if len(arrays) > 2:
              for i in range(2, len(arrays)):
                  desired_shape = paddle_backend.broadcast_shapes(
                      desired_shape, arrays[i].shape
                  )
      else:
          return [arrays[0]]
      result = []
      for tensor in arrays:
          result.append(
              paddle_backend.broadcast_to(tensor, desired_shape)
          )
      return result
  
imports: from typing import Optional, Union, Sequence, List
  import paddle
  import ivy.functional.backends.paddle
  import numpy
  import ivy
  from ivy.func_wrapper import with_unsupported_dtypes
  from ivy.functional.ivy.data_type import _handle_nestable_dtype_info
  from .None import backend_version
label_desc: Use image node_id 2 for calling a global function with example usage: broadcast_arrays() and returns: result, list