code: def _build_variable(
      self, ndarray, name=None, dtype=None, static_rank=False
  ):
      if dtype is None:
          dtype = self.dtype
      ndarray = np.array(ndarray, dtype=dtype)
      if self.use_static_shape:
          return tf.Variable(ndarray, name=name, dtype=dtype)
      elif static_rank:
          return tf.Variable(
              ndarray,
              name=name,
              dtype=dtype,
              shape=tf.TensorShape([None] * len(ndarray.shape)),
          )
      else:
          return tf.Variable(
              ndarray,
              name=name,
              dtype=dtype,
              shape=tf.TensorShape(None),
          )
  
imports: import numpy
  import tensorflow.compat.v1
  import tensorflow.compat.v2
  from tensorflow_probability.python.distributions import bernoulli
  from tensorflow_probability.python.distributions import categorical
  from tensorflow_probability.python.distributions import dirichlet
  from tensorflow_probability.python.distributions import exponential
  from tensorflow_probability.python.distributions import independent
  from tensorflow_probability.python.distributions import logistic
  from tensorflow_probability.python.distributions import mixture_same_family
  from tensorflow_probability.python.distributions import mvn_diag
  from tensorflow_probability.python.distributions import normal
  from tensorflow_probability.python.internal import hypothesis_testlib
  from tensorflow_probability.python.internal import samplers
  from tensorflow_probability.python.internal import tensorshape_util
  from tensorflow_probability.python.internal import test_util
  from tensorflow_probability.python.math import gradient
label_desc: Use image node_id 30 for calling the _MixtureSameFamilyTest obj's underlying member method code with example usage: obj._build_variable(ndarray, name, dtype, static_rank) and returns: tf, tf, tf