from __future__ import absolute_import
from __future__ import division
import numpy as np

class InstanceNormalization_1:

	m_consumed_inputs = list()
	m_epsilon = float()
	def __init__(self, _name: str, _tensor: dict, consumed_inputs=list(), epsilon=float()):
		self.name = _name
		self.tensor = _tensor
		self.m_consumed_inputs = consumed_inputs
		self.m_epsilon = epsilon

	def output(self, output=str()):
		self.m_output = output


	def __call__(self, input=str(), scale=str(), B=str()):
		self.m_input = input
		self.m_scale = scale
		self.m_B = B

		return (self.tensor[self.m_output])


class InstanceNormalization_6:

	m_epsilon = float()
	def __init__(self, _name: str, _tensor: dict, epsilon=float()):
		self.name = _name
		self.tensor = _tensor
		self.m_epsilon = epsilon

	def output(self, output=str()):
		self.m_output = output


	def __call__(self, input=str(), scale=str(), B=str()):
		self.m_input = input
		self.m_scale = scale
		self.m_B = B

		return (self.tensor[self.m_output])

9b7b316019b6c8c736c133e457bb4c9abdb6cf2e
<mask token>


class InstanceNormalization_1:
    <mask token>
    <mask token>

    def __init__(self, _name: str, _tensor: dict, consumed_inputs=list(),
        epsilon=float()):
        self.name = _name
        self.tensor = _tensor
        self.m_consumed_inputs = consumed_inputs
        self.m_epsilon = epsilon
    <mask token>

    def __call__(self, input=str(), scale=str(), B=str()):
        self.m_input = input
        self.m_scale = scale
        self.m_B = B
        return self.tensor[self.m_output]


class InstanceNormalization_6:
    m_epsilon = float()

    def __init__(self, _name: str, _tensor: dict, epsilon=float()):
        self.name = _name
        self.tensor = _tensor
        self.m_epsilon = epsilon

    def output(self, output=str()):
        self.m_output = output

    def __call__(self, input=str(), scale=str(), B=str()):
        self.m_input = input
        self.m_scale = scale
        self.m_B = B
        return self.tensor[self.m_output]

<mask token>


class InstanceNormalization_1:
    <mask token>
    <mask token>

    def __init__(self, _name: str, _tensor: dict, consumed_inputs=list(),
        epsilon=float()):
        self.name = _name
        self.tensor = _tensor
        self.m_consumed_inputs = consumed_inputs
        self.m_epsilon = epsilon

    def output(self, output=str()):
        self.m_output = output

    def __call__(self, input=str(), scale=str(), B=str()):
        self.m_input = input
        self.m_scale = scale
        self.m_B = B
        return self.tensor[self.m_output]


class InstanceNormalization_6:
    m_epsilon = float()

    def __init__(self, _name: str, _tensor: dict, epsilon=float()):
        self.name = _name
        self.tensor = _tensor
        self.m_epsilon = epsilon

    def output(self, output=str()):
        self.m_output = output

    def __call__(self, input=str(), scale=str(), B=str()):
        self.m_input = input
        self.m_scale = scale
        self.m_B = B
        return self.tensor[self.m_output]

<mask token>


class InstanceNormalization_1:
    m_consumed_inputs = list()
    m_epsilon = float()

    def __init__(self, _name: str, _tensor: dict, consumed_inputs=list(),
        epsilon=float()):
        self.name = _name
        self.tensor = _tensor
        self.m_consumed_inputs = consumed_inputs
        self.m_epsilon = epsilon

    def output(self, output=str()):
        self.m_output = output

    def __call__(self, input=str(), scale=str(), B=str()):
        self.m_input = input
        self.m_scale = scale
        self.m_B = B
        return self.tensor[self.m_output]


class InstanceNormalization_6:
    m_epsilon = float()

    def __init__(self, _name: str, _tensor: dict, epsilon=float()):
        self.name = _name
        self.tensor = _tensor
        self.m_epsilon = epsilon

    def output(self, output=str()):
        self.m_output = output

    def __call__(self, input=str(), scale=str(), B=str()):
        self.m_input = input
        self.m_scale = scale
        self.m_B = B
        return self.tensor[self.m_output]

from __future__ import absolute_import
from __future__ import division
import numpy as np


class InstanceNormalization_1:
    m_consumed_inputs = list()
    m_epsilon = float()

    def __init__(self, _name: str, _tensor: dict, consumed_inputs=list(),
        epsilon=float()):
        self.name = _name
        self.tensor = _tensor
        self.m_consumed_inputs = consumed_inputs
        self.m_epsilon = epsilon

    def output(self, output=str()):
        self.m_output = output

    def __call__(self, input=str(), scale=str(), B=str()):
        self.m_input = input
        self.m_scale = scale
        self.m_B = B
        return self.tensor[self.m_output]


class InstanceNormalization_6:
    m_epsilon = float()

    def __init__(self, _name: str, _tensor: dict, epsilon=float()):
        self.name = _name
        self.tensor = _tensor
        self.m_epsilon = epsilon

    def output(self, output=str()):
        self.m_output = output

    def __call__(self, input=str(), scale=str(), B=str()):
        self.m_input = input
        self.m_scale = scale
        self.m_B = B
        return self.tensor[self.m_output]

from __future__ import absolute_import
from __future__ import division
import numpy as np

class InstanceNormalization_1:

	m_consumed_inputs = list()
	m_epsilon = float()
	def __init__(self, _name: str, _tensor: dict, consumed_inputs=list(), epsilon=float()):
		self.name = _name
		self.tensor = _tensor
		self.m_consumed_inputs = consumed_inputs
		self.m_epsilon = epsilon

	def output(self, output=str()):
		self.m_output = output


	def __call__(self, input=str(), scale=str(), B=str()):
		self.m_input = input
		self.m_scale = scale
		self.m_B = B

		return (self.tensor[self.m_output])


class InstanceNormalization_6:

	m_epsilon = float()
	def __init__(self, _name: str, _tensor: dict, epsilon=float()):
		self.name = _name
		self.tensor = _tensor
		self.m_epsilon = epsilon

	def output(self, output=str()):
		self.m_output = output


	def __call__(self, input=str(), scale=str(), B=str()):
		self.m_input = input
		self.m_scale = scale
		self.m_B = B

		return (self.tensor[self.m_output])
