code: def _SparseSliceGrad(op: ops.Operation, *grads):
      """The backward operator for the SparseSlice op.
  
      This op takes in the upstream gradient w.r.t. non-empty values of
      the sliced `SparseTensor`, and outputs the gradients w.r.t.
      the non-empty values of input `SparseTensor`.
  
      Args:
        op: the SparseSlice op
        *grads: the incoming gradients, one element per output of `op`
  
      Returns:
        Gradient for each of the 5 input tensors of SparseSlice:
          (indices, values, shape, start, size)
        The gradients for the indices, shape, start and the size are None.
      """
      backprop_val_grad = grads[1]
      input_indices = op.inputs[0]
      input_start = op.inputs[3]
      output_indices = op.outputs[0]
  
      val_grad = gen_sparse_ops.sparse_slice_grad(
          backprop_val_grad, input_indices, input_start, output_indices
      )
      val_grad.set_shape(op.inputs[1].get_shape())
      # (indices, values, shape, start, size)
      return (None, val_grad, None, None, None)
  
imports: from tensorflow.python.framework import dtypes
  from tensorflow.python.framework import ops
  from tensorflow.python.framework import sparse_tensor
  from tensorflow.python.ops import array_ops
  from tensorflow.python.ops import gen_sparse_ops
  from tensorflow.python.ops import math_ops
  from tensorflow.python.ops import sparse_ops
label_desc: Use image node_id 5 for calling a global function with example usage: _SparseSliceGrad(op) and returns: None,  val_grad,  None,  None,  None