code: def infer_channel_first(x: np.array) -> bool:
      """
      Infer if the channels are first.
  
      Assumes:
          For 1D input:
              nr_channels < sequence_length
  
          For 2D input:
              nr_channels < img_width and nr_channels < img_height
  
      For higher dimensional input an error is raised.
      For input in n_features x n_batches format True is returned (no channel).
  
      Parameters
      ----------
      x: np.ndarray
           Input image.
  
      Returns
      -------
      For 1D input:
          True for input shape (nr_batch, nr_features).
  
      For 2D input:
          True if input shape is (nr_batch, nr_channels, sequence_length).
          False if input shape is (nr_batch, sequence_length, nr_channels).
          An error is raised if the two last dimensions are equal.
  
      For 3D input:
          True if input shape is (nr_batch, nr_channels, img_width, img_height).
          False if input shape is (nr_batch, img_width, img_height, nr_channels).
          An error is raised if the three last dimensions are equal.
      """
      err_msg = (
          "Ambiguous input shape. Cannot infer channel-first/channel-last order. "
          "Try setting the `channel_first` argument"
      )
  
      # Check for 2D input (n_features x n_batches), return True (no channel dimension).
      if len(np.shape(x)) == 2:
          return True
  
      # Check for 1D input with a channel dimension
      if len(np.shape(x)) == 3:
          # If second last dimension is smaller, channels are first.
          if np.shape(x)[-2] < np.shape(x)[-1]:
              return True
          # If second last dimension is larger, channels are last.
          elif np.shape(x)[-2] > np.shape(x)[-1]:
              return False
          else:
              raise ValueError(err_msg)
  
      elif len(np.shape(x)) == 4:
          # If last dimension is smallest, channels are last.
          if (
              np.shape(x)[-1] < np.shape(x)[-2]
              and np.shape(x)[-1] < np.shape(x)[-3]
          ):
              return False
          # If third last dimension is smallest, channels are first.
          if (
              np.shape(x)[-3] < np.shape(x)[-1]
              and np.shape(x)[-3] < np.shape(x)[-2]
          ):
              return True
          raise ValueError(err_msg)
  
      else:
          raise ValueError(
              "Only batched 1D and 2D multi-channel input dimensions supported (excluding the channels)."
          )
  
imports: import copy
  import re
  from importlib import util
  from typing import Any, Dict, Optional, Sequence, Tuple, Union, List, TypeVar
  import numpy
  from skimage.segmentation import slic, felzenszwalb
  from quantus.helpers import asserts
  from quantus.helpers.model.model_interface import ModelInterface
label_desc: Use image node_id 7 for calling a global function with example usage: infer_channel_first(x) and returns: True, True, False, False, True