code: def test_reduce_front_mean(self, num_reduce_dim, gc, dc):
      X = np.random.rand(6, 7, 8, 2).astype(np.float32)
  
      def ref_mean(X):
          return [np.mean(X, axis=(tuple(range(num_reduce_dim))))]
  
      self.reduce_op_test(
          "ReduceFrontMean",
          ref_mean,
          [X],
          ["input"],
          num_reduce_dim,
          gc,
      )
      self.grad_variant_input_test(
          "ReduceFrontMeanGradient", X, ref_mean, num_reduce_dim
      )
  
imports: from caffe2.python import core, workspace
  from hypothesis import given, settings
  import caffe2.python.hypothesis_test_util
  import caffe2.python.serialized_test.serialized_test_util
  import hypothesis.strategies
  import numpy
  import itertools
label_desc: Use image node_id 7 for calling the TestReduceFrontReductions obj's underlying member method code with example usage: obj.test_reduce_front_mean(num_reduce_dim, gc, dc) and returns: list