code: def _check_shape_and_type_consistency(
      preds: Tensor, target: Tensor
  ) -> Tuple[DataType, int]:
      """Check that the shape and type of inputs are consistent with each other.
  
      The input types needs to be one of allowed input types (see the documentation of docstring of
      ``_input_format_classification``). It does not check for consistency of number of classes, other functions take
      care of that.
  
      It returns the name of the case in which the inputs fall, and the implied number of classes (from the ``C`` dim for
      multi-class data, or extra dim(s) for multi-label data).
  
      """
      preds_float = preds.is_floating_point()
  
      if preds.ndim == target.ndim:
          if preds.shape != target.shape:
              raise ValueError(
                  "The `preds` and `target` should have the same shape,",
                  f" got `preds` with shape={preds.shape} and `target` with shape={target.shape}.",
              )
          if preds_float and target.numel() > 0 and target.max() > 1:
              raise ValueError(
                  "If `preds` and `target` are of shape (N, ...) and `preds` are floats, `target` should be binary."
              )
  
          # Get the case
          if preds.ndim == 1 and preds_float:
              case = DataType.BINARY
          elif preds.ndim == 1 and not preds_float:
              case = DataType.MULTICLASS
          elif preds.ndim > 1 and preds_float:
              case = DataType.MULTILABEL
          else:
              case = DataType.MULTIDIM_MULTICLASS
          implied_classes = preds[0].numel() if preds.numel() > 0 else 0
  
      elif preds.ndim == target.ndim + 1:
          if not preds_float:
              raise ValueError(
                  "If `preds` have one dimension more than `target`, `preds` should be a float tensor."
              )
          if preds.shape[2:] != target.shape[1:]:
              raise ValueError(
                  "If `preds` have one dimension more than `target`, the shape of `preds` should be"
                  " (N, C, ...), and the shape of `target` should be (N, ...)."
              )
  
          implied_classes = preds.shape[1] if preds.numel() > 0 else 0
  
          case = (
              DataType.MULTICLASS
              if preds.ndim == 2
              else DataType.MULTIDIM_MULTICLASS
          )
      else:
          raise ValueError(
              "Either `preds` and `target` both should have the (same) shape (N, ...), or `target` should be (N, ...)"
              " and `preds` should be (N, C, ...)."
          )
  
      return case, implied_classes
  
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 4 for calling a global function with example usage: _check_shape_and_type_consistency(preds, target) and returns: case,  implied_classes