code: def linear_interpolate_rank(tensor1, tensor2, coeffs, rank=1):
      """Linearly interpolate channel at "rank" between two tensors.
  
      The channels are ranked according to their L2 norm between tensor1[channel]
      and tensor2[channel].
  
      Args:
        tensor1: 4-D Tensor, NHWC
        tensor2: 4-D Tensor, NHWC
        coeffs: list of floats.
        rank: integer.
      Returns:
        interp_latents: list of interpolated 4-D Tensors, shape=(NHWC)
      """
      # sum across space, max across channels.
      _, _, _, num_channels = common_layers.shape_list(tensor1)
      diff_sq_sum = tf.reduce_sum(
          (tensor1 - tensor2) ** 2, axis=(0, 1, 2)
      )
      _, feature_ranks = tf.math.top_k(diff_sq_sum, k=rank)
      feature_rank = feature_ranks[-1]
      channel_inds = tf.range(num_channels, dtype=tf.int32)
      channel_mask = tf.equal(channel_inds, feature_rank)
      ones_t = tf.ones(num_channels, dtype=tf.float32)
      zeros_t = tf.zeros(num_channels, dtype=tf.float32)
  
      interp_tensors = []
      for coeff in coeffs:
          curr_coeff = tf.where(channel_mask, coeff * ones_t, zeros_t)
          interp_tensor = tensor1 + curr_coeff * (tensor2 - tensor1)
          interp_tensors.append(interp_tensor)
      return tf.concat(interp_tensors, axis=0)
  
imports: from __future__ import absolute_import
  from __future__ import division
  from __future__ import print_function
  import functools
  import numpy
  import scipy
  from tensor2tensor.layers import common_layers
  from tensor2tensor.layers import common_video
  from tensor2tensor.utils import contrib
  import tensorflow.compat.v1
  from tensorflow.compat.v1 import estimator
  import tensorflow_probability
label_desc: Use image node_id 2 for calling a global function with example usage: linear_interpolate_rank(tensor1, tensor2, coeffs, rank) and returns: tf