class_docstr: ImageSegmenter handler class. This handler takes a batch of images
  and returns output shape as [N K H W],
  where N - batch size, K - number of classes, H - height and W - width.
class_docstr_tok: ["ImageSegmenter","handler","class",".","This","handler","takes","a","batch","of","images","and","returns","output","shape","as","[","N","K","H","W","]",",","where","N","-","batch","size",",","K","-","number","of","classes",",","H","-","height","and","W","-","width","."]
code: def postprocess(self, data):
      # Returning the class for every pixel makes the response size too big
      # (> 24mb). Instead, we'll only return the top class for each image
      data = data["out"]
      data = torch.nn.functional.softmax(data, dim=1)
      data = torch.max(data, dim=1)
      data = torch.stack(
          [data.indices.type(data.values.dtype), data.values], dim=3
      )
  
      return data.tolist()
  
imports: import torch
  from torchvision import transforms
  from .vision_handler import VisionHandler
label_desc: Use image node_id 1 for calling the ImageSegmenter obj's underlying member method code with example usage: obj.postprocess(data) and returns: data