code: def _reduction_axes(self, input_shape, event_dims):
      if isinstance(event_dims, int):
          event_dims = [event_dims]
      ndims = len(input_shape)
      # Convert event_dims to non-negative indexing.
      event_dims = list(event_dims)
      for idx, x in enumerate(event_dims):
          if x < 0:
              event_dims[idx] = ndims + x
      return tuple(i for i in range(ndims) if i not in event_dims)
  
imports: from absl.testing import parameterized
  import numpy
  import tensorflow.compat.v1
  import tensorflow.compat.v2
  from tensorflow_probability.python.bijectors import batch_normalization
  from tensorflow_probability.python.bijectors import bijector_test_util
  from tensorflow_probability.python.bijectors import invert
  from tensorflow_probability.python.bijectors import reshape
  from tensorflow_probability.python.distributions import mvn_diag
  from tensorflow_probability.python.distributions import normal
  from tensorflow_probability.python.distributions import sample
  from tensorflow_probability.python.distributions import transformed_distribution
  from tensorflow_probability.python.internal import test_util
  from tensorflow_probability.python.internal import tf_keras
label_desc: Use image node_id 1 for calling the BatchNormTest obj's underlying member method code with example usage: obj._reduction_axes(input_shape, event_dims) and returns: tuple