%outputs_8, %control_9 = tf_executor.island wraps "tf.FloorDiv"(%outputs_6, %outputs) {device = ""} : (tensor<i32>, tensor<i32>) -> tensor<i32>
      tf_executor.fetch %outputs_8 : tensor<i32>
    }
    func.return %0 : tensor<i32>
  }
}

// -----

module attributes {tf_saved_model.semantics} {
  // CHECK-LABEL: func @removes_dead_code

  EXPECT_EQ(item.fetch.size(), num_values);
  EXPECT_EQ(list_total_size, storage_size);

  std::unique_ptr<char*[]> values(new char*[item.fetch.size()]);
  std::unique_ptr<size_t[]> lens(new size_t[item.fetch.size()]);
  std::unique_ptr<char[]> storage(new char[storage_size]);
  TF_GetFetchNodesList(c_item, values.get(), lens.get(), item.fetch.size(),

#------------------------------------------------------------------------
##fetch-root-ca:	fetch root CA  and key from a k8s cluster.
.PHONY: fetch-root-ca
rawcluster := $(shell kubectl config current-context)
cluster := $(subst /,-,$(rawcluster))
pwd := $(shell pwd)
export KUBECONFIG

fetch-root-ca:
	@echo "fetching root ca from k8s cluster: "$(cluster)""
	@mkdir -p $(pwd)/$(cluster)

  }
}
feed {
  id { node_name: "y_const" }
  shape {
    dim { size: 1 }
  }
}
feed {
  id { node_name: "z_identity"}
  shape {
    dim { size: 1 }
  }
}
fetch {
  id { node_name: "x_y_sum" }
}
fetch {
  id { node_name: "x_z_sum" }

  func.func @main() {
    tf_executor.graph {
      %control = tf_executor.island {
        tf_executor.yield
      }
      tf_executor.fetch %control : !tf_executor.control
    }
    tf_executor.graph {
      %control = tf_executor.island {
        tf_executor.yield
      }
      tf_executor.fetch %control : !tf_executor.control
    }
    return
  }

    dim { size: 2 }
  }
  name: "x"
}
feed {
  id { node_name: "y_hold" }
  shape {
    dim { size: 2 }
    dim { size: 2 }
  }
  name: "y"
}
fetch {
  id { node_name: "x_y_prod" }
  name: "x_y_prod"
}
fetch {
  id { node_name: "x_y_sum" }
  name: "x_y_sum"

      }) {device = "CPU:0"} : () -> (tensor<f32>, tensor<i32>)
      %c = "tf.opC"() : () -> tensor<i1>
      tf_executor.yield %a, %launch#0, %launch#1, %c : tensor<i1>, tensor<f32>, tensor<i32>, tensor<i1>
    }
    tf_executor.fetch
  }
  func.return
}

// CHECK:      %[[A:.*]] = "tf.opA"
// CHECK:      %[[B:.*]]:2 = "tf.opB"(%[[A]])
// CHECK-SAME: device = "CPU:0"
// CHECK:      %[[C:.*]] = "tf.opC"
// CHECK-NOT:  "tf_device.launch"

     */
    public void setUrl(String url) {
        setUrl(URI.create(url));
    }

    /**
     * Sets the URL of the cache. The URL must end in a '/'.
     */
    public void setUrl(URL url) throws URISyntaxException {
        setUrl(url.toURI());
    }

    /**
     * Sets the URL of the cache. The URL must end in a '/'.
     */
    public void setUrl(@Nullable URI url) {
        this.url = url;

      %island = tf_executor.island {
        "tf.OpA"() : () -> ()
        "tf.OpB"() : () -> ()
        tf_executor.yield
      }
      tf_executor.fetch
    }
    func.return
  }
}

// -----

// Check that we report an unexpected dependency path between two stateless ops.
func.func @two_stateless_ops() -> () {
  tf_executor.graph {

    public void test_checkMaxContentLength() {
        AbstractCrawlerClient client = new AbstractCrawlerClient() {
        };
        ResponseData responseData = new ResponseData();
        responseData.setUrl("http://test.com/");

        client.checkMaxContentLength(responseData);

        responseData.setContentLength(-1);
        client.checkMaxContentLength(responseData);

        responseData.setContentLength(1000L);