code: def _compute_gradient_list(f, xs, delta):
      """Compute gradients for a list of x values."""
      # convert xs to tensors so that dtype and shape have uniform types
      xs = [ops.convert_to_tensor(x) for x in xs]
      # run the function to get info of the result
      xs_dtypes = [x.dtype for x in xs]
      xs_shapes = [x.shape for x in xs]
      f_temp = _prepare(f, xs_dtypes, xs_shapes)
      y = f_temp(*xs)
      return tuple(
          zip(
              *[
                  _compute_gradient(
                      f, y.shape, dtypes.as_dtype(y.dtype), xs, i, delta
                  )
                  for i in range(len(xs))
              ]
          )
      )
  
imports: import numpy
  from tensorflow.python.eager import backprop
  from tensorflow.python.eager import context
  from tensorflow.python.framework import dtypes
  from tensorflow.python.framework import indexed_slices
  from tensorflow.python.framework import ops
  from tensorflow.python.framework import tensor
  from tensorflow.python.ops import array_ops
  from tensorflow.python.ops import gradients_impl
  from tensorflow.python.platform import tf_logging
  from tensorflow.python.util.tf_export import tf_export
label_desc: Use image node_id 8 for calling a global function with example usage: _compute_gradient_list(f, xs, delta) and returns: tuple