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Solve the following problem using Python, implementing the functions described below, one line at a time
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Description:
 def get_variable_dtype( master_dtype=tf.bfloat16, slice_dtype=tf.float32, activation_dtype=tf.float32):
 """Datatypes to use for the run. Args: master_dtype: string, datatype for checkpoints keep this the same between training and eval/inference slice_dtype: string, datatype for variables in memory must be tf.float32 for training activation_dtype: string, datatype for activations less memory usage if tf.bfloat16 but possible numerical issues Returns: a mtf.VariableDtype """

  return mtf.VariableDType(
      master_dtype=tf.as_dtype(master_dtype),
      slice_dtype=tf.as_dtype(slice_dtype),
      activation_dtype=tf.as_dtype(activation_dtype))