code: def _dynamic_gaussian_memory_lookup_grad(
      op, mean_grad, variance_grad, distance_grad, cluster_grad
  ):
      """The gradient for DynamicGaussianMemoryLookup.
  
      The (mean, variance, distance, cluster_ids) are updated inside the dynamic
      memory based on the input, so we just ignore them. There is also no need to
      back-propagate the gradients for the input since there is no close-form
      formula for, e.g., mean(inputs). Instead, the gradients of the input are
      updated through other parts of a computation graph, e.g., from the loss of
      (input - mean)^2. This is consistent with the batch-normalization as its
      special case.
  
      Args:
        op: The dynamic_gaussian_memory_lookup op.
        mean_grad: A tensor representing the gradient w.r.t. the first output.
        variance_grad: A tensor representing the gradient w.r.t. the second output.
        distance_grad: A tensor representing the gradient w.r.t. the third output.
        cluster_grad: A tensor representing the gradient w.r.t. the fourth output.
  
      Returns:
        The gradients w.r.t. the input.
      """
      del mean_grad
      del variance_grad
      del distance_grad
      del cluster_grad
      # Grads for `inputs` and `mode`.
      grads = [
          tf.zeros_like(op.inputs[i]) for i in range(len(op.inputs) - 1)
      ]
      # grad for `resource` input. tf.zeros_like only accept Tensor-like input.
      grads.append(0)
      return grads
  
imports: import typing
  from research.carls import context
  from research.carls import dynamic_embedding_config_pb2
  from research.carls.kernels import gen_carls_ops
  import tensorflow
label_desc: Use image node_id 2 for calling a global function with example usage: _dynamic_gaussian_memory_lookup_grad(op, mean_grad, variance_grad, distance_grad, cluster_grad) and returns: grads