class_docstr: Base class for segmentation networks.
  
  Args:
      opts: Command-line arguments
      encoder: Image classification network
docstr: Create dummy input and labels for CI/CD purposes. Child classes must override it
  if functionality is different.
docstr_tok: ["Create","dummy","input","and","labels","for","CI\/CD","purposes",".","Child","classes","must","override","it","if","functionality","is","different","."]
code: def dummy_input_and_label(self, batch_size: int) -> Dict:
      """Create dummy input and labels for CI/CD purposes. Child classes must override it
      if functionality is different.
      """
      img_channels = 3
      height = 224
      width = 224
      n_classes = 10
      img_tensor = torch.randn(
          batch_size, img_channels, height, width, dtype=torch.float
      )
      label_tensor = torch.randint(
          low=0, high=n_classes, size=(batch_size, height, width)
      ).long()
      return {"samples": img_tensor, "targets": label_tensor}
  
file_path: ml-cvnets/cvnets/models/segmentation/base_seg.py
imports: import argparse
  from typing import Any, Dict
  import torch
  from cvnets.models import MODEL_REGISTRY, BaseAnyNNModel
  from cvnets.models.classification.base_image_encoder import BaseImageEncoder
label_desc: Use image node_id 5 for calling the BaseSegmentation obj's underlying member method code with example usage: obj.dummy_input_and_label(batch_size) and returns: dict