// all the gradients from the shape it fills.
  // We use ReduceSum to implement this, which needs an argument providing
  // the indices of all the dimensions of the incoming gradient.
  // grad(x) = reduce_sum(grad(y), [0..rank(grad(y))])
  auto all_dims = Range(scope, Const(scope, 0), Rank(scope, grad_inputs[0]),
                        Const(scope, 1));
  grad_outputs->push_back(ReduceSum(scope, grad_inputs[0], all_dims));
  return scope.status();

      mlir::dyn_cast_or_null<RankedTensorType>(op.getOperand(0).getType());
  if (!lhs) {
    return failure();
  }
  // unary einsum op is only supported to the case where the operation can be
  // replaced using reduce_sum and/or transpose
  if (const auto dnums_or =
          GetEinsumDimensionNumbersUnary(op.getEquation(), lhs)) {
    return rewriteToReduceSumAndTranspose(op, dnums_or.value(), rewriter);
  }

  auto iota_shape = iota_type.getShape();
  auto reduce_dim = (*dimensions.value_begin<APInt>()).getSExtValue();
  if (reduce_dim < 0) reduce_dim += iota_type.getRank();

  auto index =
      std::optional<SmallVector<int64_t>>(std::in_place, iota_type.getRank());
  while (index.has_value()) {
    StridedArrayView<DenseIntElementsAttr> array_view(
        iota_const_attr, iota_shape, *index, reduce_dim);