code: def transform_img(
      image, affine, voxsize=None, init_shape=(256, 256, 256), scale=2
  ):
      r"""
      Function to reshape image as an input to the model
  
      Parameters
      ----------
      image : np.ndarray
          Image to transform to voxelspace
      affine : np.ndarray
          Affine matrix provided by the file
      voxsize : np.ndarray (3,), optional
          Voxel size of the image
      init_shape : tuple, optional
          Initial shape to transform the image to
          Default is (256, 256, 256)
      scale : float, optional
          How much we want to scale the image
          Default is 2
  
      Returns
      -------
      transformed_img : np.ndarray
      """
      if voxsize is not None and np.any(voxsize != np.ones(3)):
          image, affine2 = reslice(image, affine, voxsize, (1, 1, 1))
      else:
          affine2 = affine.copy()
      ori_shape = np.array(image.shape)
      affine2[:3, 3] += np.array(
          [init_shape[0] // 2, init_shape[1] // 2, init_shape[2] // 2]
      )
      inv_affine = np.linalg.inv(affine2)
      transformed_img = affine_transform(
          image, inv_affine, output_shape=init_shape
      )
      transformed_img, _ = reslice(
          transformed_img, np.eye(4), (1, 1, 1), (scale, scale, scale)
      )
      return transformed_img, affine2, ori_shape
  
file_path: dipy/dipy/nn/utils.py
imports: import numpy
  from scipy.ndimage import affine_transform, label
  from dipy.align.reslice import reslice
label_desc: Use image node_id 4 for calling a global function with example usage: transform_img(image, affine, voxsize, init_shape, scale) and returns: transformed_img,  affine2,  ori_shape