docstr: Check if the provided image contains multiple channels
  
  Args:
      image_np : Expected shape (channels, width, height, batch)
  
  Returns:
      bool: If this is a multi-channel image or not
docstr_tok: ["Check","if","the","provided","image","contains","multiple","channels","Args",":","image_np",":","Expected","shape","(","channels",",","width",",","height",",","batch",")","Returns",":","bool",":","If","this","is","a","multi-channel","image","or","not"]
code: def is_multichannel_image(self, image_np: np.ndarray) -> bool:
      """Check if the provided image contains multiple channels
  
      Args:
          image_np : Expected shape (channels, width, height, batch)
  
      Returns:
          bool: If this is a multi-channel image or not
      """
      return len(image_np.shape) == 4 and image_np.shape[0] > 1
  
imports: import logging
  import tempfile
  from typing import Any, Dict, Iterable, List, Optional, Tuple
  import itk
  import nrrd
  import numpy
  import torch
  from monai.data import MetaTensor
  from monai.data.image_writer import NibabelWriter
  from monailabel.utils.others.detection import create_slicer_detection_json
  from monailabel.utils.others.generic import file_ext
  from monailabel.utils.others.pathology import create_asap_annotations_xml, create_dsa_annotations_json
label_desc: Use image node_id 3 for calling the Writer obj's underlying member method code with example usage: obj.is_multichannel_image(image_np) and returns: unknown