code: def _compute_gradient_list(
      x,
      x_shape,
      y,
      y_shape,
      x_init_value=None,
      delta=1e-3,
      init_targets=None,
      extra_feed_dict=None,
  ):
      """Compute gradients for a list of x values."""
      assert isinstance(x, list)
      dx, dy = zip(*[_compute_dx_and_dy(xi, y, y_shape) for xi in x])
  
      if init_targets is not None:
          assert isinstance(init_targets, (list, tuple))
          for init in init_targets:
              init.run()
      if x_init_value is None:
          x_init_value = [None] * len(x)
      # pylint: disable=g-complex-comprehension
      ret = [
          _compute_gradient(
              xi,
              x_shapei,
              dxi,
              y,
              y_shape,
              dyi,
              x_init_valuei,
              delta,
              extra_feed_dict=extra_feed_dict,
          )
          for xi, x_shapei, dxi, dyi, x_init_valuei in zip(
              x, x_shape, dx, dy, x_init_value
          )
      ]
      return ret
  
imports: import numpy
  from tensorflow.python.framework import constant_op
  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
  from tensorflow.python.ops import math_ops
  from tensorflow.python.platform import tf_logging
  from tensorflow.python.util import deprecation
  from tensorflow.python.util.tf_export import tf_export
label_desc: Use image node_id 7 for calling a global function with example usage: _compute_gradient_list(x, x_shape, y, y_shape, x_init_value, delta, init_targets, extra_feed_dict) and returns: ret