class_docstr: Compute average Dice score for a set of pairs of prediction-groundtruth segmentations.
  
  It supports both multi-classes and multi-labels tasks.
  Input `y_pred` is compared with ground truth `y`.
  `y_pred` is expected to have binarized predictions and `y` can be single-channel class indices or in the
  one-hot format. The `include_background` parameter can be set to ``False`` to exclude
  the first category (channel index 0) which is by convention assumed to be background. If the non-background
  segmentations are small compared to the total image size they can get overwhelmed by the signal from the
  background. `y_preds` and `y` can be a list of channel-first Tensor (CHW[D]) or a batch-first Tensor (BCHW[D]),
  `y` can also be in the format of `B1HW[D]`.
  
  Example of the typical execution steps of this metric class follows :py:class:`monai.metrics.metric.Cumulative`.
  
  Args:
      include_background: whether to include Dice computation on the first channel of
          the predicted output. Defaults to ``True``.
      reduction: define mode of reduction to the metrics, will only apply reduction on `not-nan` values,
          available reduction modes: {``"none"``, ``"mean"``, ``"sum"``, ``"mean_batch"``, ``"sum_batch"``,
          ``"mean_channel"``, ``"sum_channel"``}, default to ``"mean"``. if "none", will not do reduction.
      get_not_nans: whether to return the `not_nans` count, if True, aggregate() returns (metric, not_nans).
          Here `not_nans` count the number of not nans for the metric, thus its shape equals to the shape of the metric.
      ignore_empty: whether to ignore empty ground truth cases during calculation.
          If `True`, NaN value will be set for empty ground truth cases.
          If `False`, 1 will be set if the predictions of empty ground truth cases are also empty.
      num_classes: number of input channels (always including the background). When this is None,
          ``y_pred.shape[1]`` will be used. This option is useful when both ``y_pred`` and ``y`` are
          single-channel class indices and the number of classes is not automatically inferred from data.
      return_with_label: whether to return the metrics with label, only works when reduction is "mean_batch".
          If `True`, use "label_{index}" as the key corresponding to C channels; if 'include_background' is True,
          the index begins at "0", otherwise at "1". It can also take a list of label names.
          The outcome will then be returned as a dictionary.
class_docstr_tok: ["Compute","average","Dice","score","for","a","set","of","pairs","of","prediction-groundtruth","segmentations",".","It","supports","both","multi-classes","and","multi-labels","tasks",".","Input","`","y_pred","`","is","compared","with","ground","truth","`","y","`",".","`","y_pred","`","is","expected","to","have","binarized","predictions","and","`","y","`","can","be","single-channel","class","indices","or","in","the","one-hot","format",".","The","`","include_background","`","parameter","can","be","set","to","``","False","``","to","exclude","the","first","category","(","channel","index","0",")","which","is","by","convention","assumed","to","be","background",".","If","the","non-background","segmentations","are","small","compared","to","the","total","image","size","they","can","get","overwhelmed","by","the","signal","from","the","background",".","`","y_preds","`","and","`","y","`","can","be","a","list","of","channel-first","Tensor","(","CHW","[","D","]",")","or","a","batch-first","Tensor","(","BCHW","[","D","]",")",",","`","y","`","can","also","be","in","the","format","of","`","B1HW","[","D","]","`",".","Example","of","the","typical","execution","steps","of","this","metric","class","follows",":","py",":","class",":","`","monai.metrics.metric.Cumulative","`",".","Args",":","include_background",":","whether","to","include","Dice","computation","on","the","first","channel","of","the","predicted","output",".","Defaults","to","``","True","``",".","reduction",":","define","mode","of","reduction","to","the","metrics",",","will","only","apply","reduction","on","`","not-nan","`","values",",","available","reduction","modes",":","{","``","``","none","''","``",",","``","``","mean","''","``",",","``","``","sum","''","``",",","``","``","mean_batch","''","``",",","``","``","sum_batch","''","``",",","``","``","mean_channel","''","``",",","``","``","sum_channel","''","``","}",",","default","to","``","``","mean","''","``",".","if","``","none","''",",","will","not","do","reduction",".","get_not_nans",":","whether","to","return","the","`","not_nans","`","count",",","if","True",",","aggregate","(",")","returns","(","metric",",","not_nans",")",".","Here","`","not_nans","`","count","the","number","of","not","nans","for","the","metric",",","thus","its","shape","equals","to","the","shape","of","the","metric",".","ignore_empty",":","whether","to","ignore","empty","ground","truth","cases","during","calculation",".","If","`","True","`",",","NaN","value","will","be","set","for","empty","ground","truth","cases",".","If","`","False","`",",","1","will","be","set","if","the","predictions","of","empty","ground","truth","cases","are","also","empty",".","num_classes",":","number","of","input","channels","(","always","including","the","background",")",".","When","this","is","None",",","``","y_pred.shape","[","1","]","``","will","be","used",".","This","option","is","useful","when","both","``","y_pred","``","and","``","y","``","are","single-channel","class","indices","and","the","number","of","classes","is","not","automatically","inferred","from","data",".","return_with_label",":","whether","to","return","the","metrics","with","label",",","only","works","when","reduction","is","``","mean_batch","''",".","If","`","True","`",",","use","``","label_","{","index","}","''","as","the","key","corresponding","to","C","channels",";","if","'include_background","'","is","True",",","the","index","begins","at","``","0","''",",","otherwise","at","``","1","''",".","It","can","also","take","a","list","of","label","names",".","The","outcome","will","then","be","returned","as","a","dictionary","."]
docstr: Args:
      y_pred: input data to compute, typical segmentation model output.
          It must be one-hot format and first dim is batch, example shape: [16, 3, 32, 32]. The values
          should be binarized.
      y: ground truth to compute mean Dice metric. `y` can be single-channel class indices or
          in the one-hot format.
  
  Raises:
      ValueError: when `y_pred` has less than three dimensions.
docstr_tok: ["Args",":","y_pred",":","input","data","to","compute",",","typical","segmentation","model","output",".","It","must","be","one-hot","format","and","first","dim","is","batch",",","example","shape",":","[","16",",","3",",","32",",","32","]",".","The","values","should","be","binarized",".","y",":","ground","truth","to","compute","mean","Dice","metric",".","`","y","`","can","be","single-channel","class","indices","or","in","the","one-hot","format",".","Raises",":","ValueError",":","when","`","y_pred","`","has","less","than","three","dimensions","."]
code: def _compute_tensor(self, y_pred: torch.Tensor, y: torch.Tensor) -> torch.Tensor:  # type: ignore[override]
      """
      Args:
          y_pred: input data to compute, typical segmentation model output.
              It must be one-hot format and first dim is batch, example shape: [16, 3, 32, 32]. The values
              should be binarized.
          y: ground truth to compute mean Dice metric. `y` can be single-channel class indices or
              in the one-hot format.
  
      Raises:
          ValueError: when `y_pred` has less than three dimensions.
      """
      dims = y_pred.ndimension()
      if dims < 3:
          raise ValueError(
              f"y_pred should have at least 3 dimensions (batch, channel, spatial), got {dims}."
          )
      # compute dice (BxC) for each channel for each batch
      return self.dice_helper(y_pred=y_pred, y=y)
  
file_path: MONAI/monai/metrics/meandice.py
imports: from __future__ import annotations
  import torch
  from monai.metrics.utils import do_metric_reduction
  from monai.utils import MetricReduction
  from .metric import CumulativeIterationMetric
label_desc: Use image node_id 2 for calling the DiceMetric obj's underlying member method code with example usage: obj._compute_tensor(y_pred, y) and returns: self