code: def _differentiable_linspace(start, stop, num, *, device, dtype=None):
      if num == 1:
          return torch.unsqueeze(start, 0)
      n_m_1 = num - 1
      increment = (stop - start) / n_m_1
      increment_tiled = increment.repeat(n_m_1)
      increments = increment_tiled * torch.linspace(
          1, n_m_1, n_m_1, device=device, dtype=dtype
      )
      res = torch.cat(
          (
              torch.unsqueeze(torch.tensor(start, dtype=dtype), 0),
              start + increments,
          ),
          0,
      )
      return res
  
imports: import copy
  from numbers import Number
  from typing import Union, List, Optional, Sequence, Tuple
  import numpy
  import torch
  from torch import Tensor
  import ivy
  from ivy.func_wrapper import with_unsupported_dtypes, with_unsupported_device_and_dtypes
  from ivy.functional.ivy.creation import _asarray_to_native_arrays_and_back, _asarray_infer_device, _asarray_infer_dtype, _asarray_handle_nestable, NestedSequence, SupportsBufferProtocol, _asarray_inputs_to_native_shapes, _remove_np_bfloat16
  from .None import backend_version
label_desc: Use image node_id 1 for calling a global function with example usage: _differentiable_linspace(start, stop, num) and returns: res, torch