code: def _flexible_bincount(x: Tensor) -> Tensor:
      """Similar to `_bincount`, but works also with tensor that do not contain continuous values.
  
      Args:
          x: tensor to count
  
      Returns:
          Number of occurrences for each unique element in x
  
      """
      # make sure elements in x start from 0
      x = x - x.min()
      unique_x = torch.unique(x)
  
      output = _bincount(x, minlength=torch.max(unique_x) + 1)  # type: ignore[arg-type]
      # remove zeros from output tensor
      return output[unique_x]
  
imports: import sys
  from typing import Any, Dict, List, Optional, Sequence, Tuple, Union
  import torch
  from lightning_utilities import apply_to_collection
  from torch import Tensor
  from torchmetrics.utilities.exceptions import TorchMetricsUserWarning
  from torchmetrics.utilities.imports import _TORCH_GREATER_EQUAL_1_12, _TORCH_GREATER_EQUAL_1_13, _XLA_AVAILABLE
  from torchmetrics.utilities.prints import rank_zero_warn
label_desc: Use image node_id 16 for calling a global function with example usage: _flexible_bincount(x) and returns: output