mmdnn/conversion/onnx/onnx_emitter.py [286:309]:
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        self.add_body(1, "{:15} = helper.make_tensor_value_info('{}', onnx.mapping.NP_TYPE_TO_TENSOR_TYPE[{}.dtype], list({}.shape))".format(
                            IR_node.variable_name + '_var',
                            IR_node.variable_name + '_var',
                            IR_node.variable_name + '_var_array',
                            IR_node.variable_name + '_var_array'))
    
        self.add_body(1, "{:15} = helper.make_tensor(name='{}', data_type=onnx.mapping.NP_TYPE_TO_TENSOR_TYPE[{}.dtype], dims={}.shape, vals={}.flatten().astype(float))".format(
                            IR_node.variable_name + '_var_init',
                            IR_node.variable_name + '_var',
                            IR_node.variable_name + '_var_array',
                            IR_node.variable_name + '_var_array',
                            IR_node.variable_name + '_var_array'))                         

        self.add_body(1, "{:15} = helper.make_node('BatchNormalization', inputs=['{}', '{}', '{}', '{}', '{}'],outputs=['{}'], epsilon={}, is_test={}, name='{}')".format(
                          IR_node.variable_name,
                          self.parent_variable_name(IR_node),
                          IR_node.variable_name + '_scale',
                          IR_node.variable_name + '_bias',
                          IR_node.variable_name + '_mean',
                          IR_node.variable_name + '_var',
                          IR_node.variable_name,
                          epsilon,
                          0 if self.phase == 'train' else 1,
                          IR_node.variable_name))
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mmdnn/conversion/onnx/onnx_emitter.py [380:402]:
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        self.add_body(1, "{:15} = helper.make_tensor_value_info('{}', onnx.mapping.NP_TYPE_TO_TENSOR_TYPE[{}.dtype], list({}.shape))".format(
                            IR_node.variable_name + '_var',
                            IR_node.variable_name + '_var',
                            IR_node.variable_name + '_var_array',
                            IR_node.variable_name + '_var_array'))
    
        self.add_body(1, "{:15} = helper.make_tensor(name='{}', data_type=onnx.mapping.NP_TYPE_TO_TENSOR_TYPE[{}.dtype], dims={}.shape, vals={}.flatten().astype(float))".format(
                            IR_node.variable_name + '_var_init',
                            IR_node.variable_name + '_var',
                            IR_node.variable_name + '_var_array',
                            IR_node.variable_name + '_var_array',
                            IR_node.variable_name + '_var_array'))   
        self.add_body(1, "{:15} = helper.make_node('BatchNormalization', inputs=['{}', '{}', '{}', '{}', '{}'],outputs=['{}'], epsilon={}, is_test={}, name='{}')".format(
                          IR_node.variable_name,
                          self.parent_variable_name(IR_node),
                          IR_node.variable_name + '_scale',
                          IR_node.variable_name + '_bias',
                          IR_node.variable_name + '_mean',
                          IR_node.variable_name + '_var',
                          IR_node.variable_name,
                          epsilon,
                          0 if self.phase == 'train' else 1,
                          IR_node.variable_name))
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