def batch_normalization(x, mean, variance, offset, scale, variance_epsilon, data_format, name=None):
with ops.name_scope(name, '<STR_LIT>', [x, mean, variance, scale, offset]):<EOL><INDENT>inv = math_ops.rsqrt(variance + variance_epsilon)<EOL>if scale is not None:<EOL><INDENT>inv *= scale<EOL><DEDENT>a = math_ops.cast(inv, x.dtype)<EOL>b = math_ops.cast(offset - mean * inv if offset is not None else -mean * inv, x.dtype)<EOL>df = {'<STR_LIT>': '<STR_LIT>', '<STR_LIT>': '<STR_LIT>'}<EOL>return _bias_add(_bias_scale(x, a, df[data_format]), b, df[data_format])<EOL><DEDENT>
Data Format aware version of tf.nn.batch_normalization.