code: def _differentiable_linspace(start, stop, num, *, dtype=None):
      start = ivy.to_native(start)
      num = paddle.to_tensor(num, stop_gradient=False)
      if num == 1:
          return paddle_backend.expand_dims(start, axis=0)
      n_m_1 = paddle_backend.subtract(num, 1)
      increment = paddle_backend.divide(
          paddle_backend.subtract(stop, start), n_m_1
      )
      increment_tiled = paddle_backend.repeat(increment, n_m_1)
      increments = paddle_backend.multiply(
          increment_tiled,
          paddle.linspace(
              1, n_m_1, n_m_1.cast(paddle.int32), dtype=dtype
          ),
      )
      if isinstance(start, int) or start.ndim == 0:
          start = paddle_backend.expand_dims(start, axis=0)
      res = paddle_backend.concat(
          (start, paddle_backend.add(start, increments)), axis=0
      )
      return res.cast(dtype)
  
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 11 for calling a global function with example usage: _differentiable_linspace(start, stop, num) and returns: res, paddle_backend