code: def _basic_input_validation(
      preds: Tensor,
      target: Tensor,
      threshold: float,
      multiclass: Optional[bool],
      ignore_index: Optional[int],
  ) -> None:
      """Perform basic validation of inputs that does not require deducing any information of the type of inputs."""
      # Skip all other checks if both preds and target are empty tensors
      if _check_for_empty_tensors(preds, target):
          return
  
      if target.is_floating_point():
          raise ValueError("The `target` has to be an integer tensor.")
  
      if (ignore_index is None and target.min() < 0) or (
          ignore_index and ignore_index >= 0 and target.min() < 0
      ):
          raise ValueError(
              "The `target` has to be a non-negative tensor."
          )
  
      preds_float = preds.is_floating_point()
      if not preds_float and preds.min() < 0:
          raise ValueError(
              "If `preds` are integers, they have to be non-negative."
          )
  
      if not preds.shape[0] == target.shape[0]:
          raise ValueError(
              "The `preds` and `target` should have the same first dimension."
          )
  
      if multiclass is False and target.max() > 1:
          raise ValueError(
              "If you set `multiclass=False`, then `target` should not exceed 1."
          )
  
      if multiclass is False and not preds_float and preds.max() > 1:
          raise ValueError(
              "If you set `multiclass=False` and `preds` are integers, then `preds` should not exceed 1."
          )
  
imports: import logging
  import multiprocessing
  import os
  from functools import partial
  from time import perf_counter
  from typing import Any, Callable, Dict, Mapping, Optional, Sequence, Tuple, no_type_check
  from unittest.mock import Mock
  import torch
  from torch import Tensor
  from torchmetrics.metric import Metric
  from torchmetrics.utilities.data import select_topk, to_onehot
  from torchmetrics.utilities.enums import DataType
label_desc: Use image node_id 3 for calling a global function with example usage: _basic_input_validation(preds, target, threshold, multiclass, ignore_index) without return types