}
  TensorShapeProto proto;
  if (num_dims < 0) {
    proto.set_unknown_rank(true);
  } else {
    for (int d = 0; d < num_dims; ++d) {
      proto.add_dim()->set_size(dims[d]);
    }
  }

  forward_op_->attrs.Set(attr_name, proto);
  return op_->SetAttrShape(attr_name, dims, num_dims);
}
absl::Status SetAttrFunction(AbstractOperation* op_, const char* attr_name,

        for (int i = 0; i < array.length; i++) {
          array[i] = counter.getAndIncrement();
        }
        builder.addAll(stream(array));
      }
    },
    ADD_IIA {
      @Override
      void doIt(ImmutableDoubleArray.Builder builder, AtomicInteger counter) {
        double[] array = new double[RANDOM.nextInt(10)];
        for (int i = 0; i < array.length; i++) {

        for (int i = 0; i < array.length; i++) {
          array[i] = counter.getAndIncrement();
        }
        builder.addAll(stream(array));
      }
    },
    ADD_IIA {
      @Override
      void doIt(ImmutableIntArray.Builder builder, AtomicInteger counter) {
        int[] array = new int[RANDOM.nextInt(10)];
        for (int i = 0; i < array.length; i++) {

        for (int i = 0; i < array.length; i++) {
          array[i] = counter.getAndIncrement();
        }
        builder.addAll(stream(array));
      }
    },
    ADD_IIA {
      @Override
      void doIt(ImmutableDoubleArray.Builder builder, AtomicInteger counter) {
        double[] array = new double[RANDOM.nextInt(10)];
        for (int i = 0; i < array.length; i++) {

  TF_DeleteTensor(tensor);
  TFE_DeleteTensorHandle(tensor_handle);

  tensorflow::AttrValue i_list_values;
  for (int i = 0; i < 4; ++i) {
    i_list_values.mutable_list()->add_i(1);
  }
  SetOpAttrValueScalar(ctx, op, i_list_values, "ksize", status);
  SetOpAttrValueScalar(ctx, op, i_list_values, "strides", status);

  tensorflow::AttrValue padding_value;