code: def _max_unpool1d_mixed_handler(
      input, indices, kernel_size, **kwargs
  ):
      dt = kwargs.get("data_format", "NCW")
      inds = indices.permute(0, 2, 1) if dt == "NWC" else indices
      flat_inds = inds.reshape((-1,))
      stride = indices.shape[-1]
      not_dup = True
      for i in range(0, flat_inds.numel(), stride):
          inds = flat_inds[i : (i + stride)]
          inds = inds.unique()
          if inds.numel() != stride:
              not_dup = False
      return not_dup
  
imports: from typing import Optional, Union, Tuple, List, Literal, Sequence, Callable
  import torch
  import math
  import ivy
  from ivy.func_wrapper import with_unsupported_dtypes, with_supported_dtypes
  from .None import backend_version
  from ivy.functional.ivy.layers import _handle_padding, _get_num_padded_values, _validate_max_pool_params, _depth_max_pooling_helper
  from ivy.functional.ivy.experimental.layers import _padding_ceil_mode, _broadcast_pooling_helper
label_desc: Use image node_id 31 for calling a global function with example usage: _max_unpool1d_mixed_handler(input, indices, kernel_size) and returns: not_dup