code: def _linspace_helper(start, stop, num, axis=None, *, dtype=None):
      num = (
          num.detach().item() if isinstance(num, paddle.Tensor) else num
      )
      start_is_array = isinstance(start, paddle.Tensor)
      stop_is_array = isinstance(stop, paddle.Tensor)
      linspace_method = paddle.linspace
      sos_shape = []
      if start_is_array:
          start_shape = start.shape
          sos_shape = start_shape
          if num == 1:
              if axis is not None:
                  return paddle_backend.expand_dims(start, axis=axis)
              else:
                  return paddle_backend.expand_dims(start, axis=-1)
          start = start.reshape((-1,))
          linspace_method = (
              _differentiable_linspace
              if not start.stop_gradient
              else paddle.linspace
          )
      if stop_is_array:
          stop_shape = stop.shape
          sos_shape = stop_shape
          if num == 1:
              return (
                  paddle_backend.ones(
                      stop_shape[:axis] + [1] + stop_shape[axis:],
                      dtype=dtype,
                  )
                  * start
              )
          stop = stop.reshape((-1,))
          linspace_method = (
              _differentiable_linspace
              if not stop.stop_gradient
              else paddle.linspace
          )
      if start_is_array and stop_is_array:
          if num < start.shape[0]:
              start = paddle_backend.expand_dims(start, axis=-1)
              stop = paddle_backend.expand_dims(stop, axis=-1)
              diff = paddle_backend.subtract(stop, start)
              inc = diff / (num - 1)
              res = [start]
              res += [start + inc * i for i in range(1, num - 1)]
              res.append(stop)
          else:
              res = [
                  linspace_method(strt, stp, num)
                  for strt, stp in zip(
                      paddle_backend.unstack(start, keepdims=True),
                      paddle_backend.unstack(stop, keepdims=True),
                  )
              ]
      elif start_is_array and not stop_is_array:
          if num < start.shape[0]:
              start = paddle_backend.expand_dims(start, axis=axis)
              diff = stop - start
              inc = diff / (num - 1)
              res = [start]
              res += [start + inc * i for i in range(1, num - 1)]
              res.append(
                  paddle.ones(start.shape).astype(start.dtype) * stop
              )
          else:
              res = [linspace_method(strt, stop, num) for strt in start]
      elif not start_is_array and stop_is_array:
          if num < stop.shape[0]:
              stop = paddle_backend.expand_dims(stop, axis=-1)
              diff = stop - start
              inc = diff / (num - 1)
              res = [paddle.ones(stop.shape).astype(stop.dtype) * start]
              res += [start + inc * i for i in range(1, num - 1)]
              res.append(stop)
          else:
              res = [linspace_method(start, stp, num) for stp in stop]
      else:
          return linspace_method(start, stop, num, dtype=dtype)
      res = paddle_backend.concat(res, axis=-1).reshape(
          sos_shape + [num]
      )
      if axis is not None:
          ndim = res.ndim
          perm = list(range(ndim - 1))
          perm.insert(axis % (ndim + 1), ndim - 1)
          res = paddle_backend.permute_dims(res, perm)
      return res
  
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 10 for calling a global function with example usage: _linspace_helper(start, stop, num, axis) and returns: res, unknown, paddle_backend, paddle_backend, linspace_method