code: def compute_miou_batch(
      prediction: Union[Tuple[Tensor, Tensor], Tensor],
      target: Tensor,
      epsilon: Optional[float] = 1e-7,
  ):
      if isinstance(prediction, Tuple) and len(prediction) == 2:
          mask = prediction[0]
          assert isinstance(mask, Tensor)
      elif isinstance(prediction, Tensor):
          mask = prediction
          assert isinstance(mask, Tensor)
      else:
          raise NotImplementedError(
              "For computing loss for segmentation task, we need prediction to be an instance of Tuple or Tensor"
          )
  
      num_classes = mask.shape[1]
      pred_mask = torch.max(mask, dim=1)[1]
      assert (
          pred_mask.dim() == 3
      ), "Predicted mask tensor should be 3-dimensional (B x H x W)"
  
      pred_mask = pred_mask.byte()
      target = target.byte()
  
      # shift by 1 so that 255 is 0
      pred_mask += 1
      target += 1
  
      pred_mask = pred_mask * (target > 0)
      inter = pred_mask * (pred_mask == target)
      area_inter = torch.histc(
          inter.float(), bins=num_classes, min=1, max=num_classes
      )
      area_pred = torch.histc(
          pred_mask.float(), bins=num_classes, min=1, max=num_classes
      )
      area_mask = torch.histc(
          target.float(), bins=num_classes, min=1, max=num_classes
      )
      area_union = area_pred + area_mask - area_inter + epsilon
      return area_inter, area_union
  
file_path: ml-cvnets/metrics/intersection_over_union.py
imports: from numbers import Number
  from typing import Any, Dict, Optional, Tuple, Union
  import numpy
  import torch
  from torch import Tensor
  from metrics import METRICS_REGISTRY
  from metrics.metric_base import AverageMetric
  from utils import logger
label_desc: Use image node_id 1 for calling a global function with example usage: compute_miou_batch(prediction, target, epsilon) and returns: area_inter,  area_union