class_docstr: Compute average Intersection over Union (IoU) score between two tensors.
  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` should be in one-hot format. You can use suitable transforms
  in ``monai.transforms.post`` first to achieve binarized values.
  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_pred` and `y` can be a list of channel-first Tensor (CHW[D]) or a batch-first Tensor (BCHW[D]).
  
  Example of the typical execution steps of this metric class follows :py:class:`monai.metrics.metric.Cumulative`.
  
  Args:
      include_background: whether to include IoU 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.
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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 IoU metric. It must be one-hot format and first dim is batch.
          The values should be binarized.
  
  Raises:
      ValueError: when `y_pred` has less than three dimensions.
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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 IoU metric. It must be one-hot format and first dim is batch.
              The values should be binarized.
  
      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 IoU (BxC) for each channel for each batch
      return compute_iou(
          y_pred=y_pred,
          y=y,
          include_background=self.include_background,
          ignore_empty=self.ignore_empty,
      )
  
file_path: MONAI/monai/metrics/meaniou.py
imports: from __future__ import annotations
  import torch
  from monai.metrics.utils import do_metric_reduction, ignore_background
  from monai.utils import MetricReduction
  from .metric import CumulativeIterationMetric
label_desc: Use image node_id 2 for calling the MeanIoU obj's underlying member method code with example usage: obj._compute_tensor(y_pred, y) and returns: compute_iou