code: def test_non_one_first_dim(self):
      # Image without first dimension as 1
      input_img_rgb = np.repeat(self.input_img_np, 3, axis=0)
      input_img_rgb_torch = torch.from_numpy(input_img_rgb)
  
      transform = UltrasoundConfidenceMapTransform(sink_mode="all")
      with self.assertRaises(ValueError):
          transform(input_img_rgb_torch)
      with self.assertRaises(ValueError):
          transform(input_img_rgb)
  
      transform = UltrasoundConfidenceMapTransform(sink_mode="mid")
      with self.assertRaises(ValueError):
          transform(input_img_rgb_torch)
      with self.assertRaises(ValueError):
          transform(input_img_rgb)
  
      transform = UltrasoundConfidenceMapTransform(sink_mode="min")
      with self.assertRaises(ValueError):
          transform(input_img_rgb_torch)
      with self.assertRaises(ValueError):
          transform(input_img_rgb)
  
      transform = UltrasoundConfidenceMapTransform(sink_mode="mask")
      with self.assertRaises(ValueError):
          transform(input_img_rgb_torch, self.input_mask_torch)
      with self.assertRaises(ValueError):
          transform(input_img_rgb, self.input_mask_np)
  
file_path: MONAI/tests/test_ultrasound_confidence_map_transform.py
imports: from __future__ import annotations
  import unittest
  import numpy
  import torch
  from monai.transforms import UltrasoundConfidenceMapTransform
  from tests.utils import assert_allclose
label_desc: Use image node_id 5 for calling the TestUltrasoundConfidenceMapTransform obj's underlying member method code with example usage: obj.test_non_one_first_dim() without return types