How to use the einops._backends.AbstractBackend function in einops

return self.cupy.asnumpy(x)

    def arange(self, start, stop):
        return self.cupy.arange(start, stop)

    def stack_on_zeroth_dimension(self, tensors: list):
        return self.cupy.stack(tensors)

    def tile(self, x, repeats):
        return self.cupy.tile(x, repeats)

    def is_float_type(self, x):
        return x.dtype in ('float16', 'float32', 'float64', 'float128')


class ChainerBackend(AbstractBackend):
    framework_name = 'chainer'

    def __init__(self):
        import chainer
        import numpy
        self.numpy = numpy
        self.chainer = chainer

    def is_appropriate_type(self, tensor):
        return isinstance(tensor, self.chainer.Variable)

    def from_numpy(self, x):
        return self.chainer.Variable(x.astype('float32'))

    def to_numpy(self, x):
        if isinstance(x, self.chainer.Variable):

def __init__(self):
        super(JaxBackend, self).__init__()
        self.onp = self.np

        import jax.numpy
        self.np = jax.numpy

    def from_numpy(self, x):
        return self.np.asarray(x)

    def to_numpy(self, x):
        return self.onp.asarray(x)


class GluonBackend(AbstractBackend):
    framework_name = 'mxnet.ndarray'

    def __init__(self):
        import mxnet
        self.mx = mxnet

    def is_appropriate_type(self, tensor):
        return isinstance(tensor, self.mx.nd.NDArray)

    def from_numpy(self, x):
        var = self.mx.nd.array(x, dtype=x.dtype)
        var.attach_grad()
        return var

    def to_numpy(self, x):
        return self.mx.nd.NDArray.asnumpy(x)

return self

    def __eq__(self, other):
        return True  # we don't know actual size

    def __mul__(self, other):
        return self

    def __rmul__(self, other):
        return self

    def __hash__(self):
        return None.__hash__()


class NumpyBackend(AbstractBackend):
    framework_name = 'numpy'

    def __init__(self):
        import numpy
        self.np = numpy

    def is_appropriate_type(self, tensor):
        return isinstance(tensor, self.np.ndarray)

    def from_numpy(self, x):
        return x

    def to_numpy(self, x):
        return x

    def arange(self, start, stop):

def stack_on_zeroth_dimension(self, tensors: list):
        return self.mx.nd.stack(*tensors)

    def tile(self, x, repeats):
        return self.mx.nd.tile(x, repeats)

    def is_float_type(self, x):
        return 'float' in str(x.dtype)

    def layers(self):
        from .layers import gluon
        return gluon


class MXNetBackend(AbstractBackend):
    framework_name = 'mxnet.symbol'

    def __init__(self):
        import mxnet
        self.mx = mxnet

    def is_appropriate_type(self, tensor):
        return isinstance(tensor, self.mx.symbol.Symbol)

    def create_symbol(self, shape, dtype='float32'):
        # mxnet accepts zeros as undefined dimensions
        shape = tuple(0 if d is None else d for d in shape)
        var = self.mx.symbol.Variable('input', shape=shape, dtype=dtype)
        return var

    def eval_symbol(self, symbol, input_dict):

def stack_on_zeroth_dimension(self, tensors: list):
        return self.torch.stack(tensors)

    def tile(self, x, repeats):
        return x.repeat(repeats)

    def is_float_type(self, x):
        return x.dtype in [self.torch.float16, self.torch.float32, self.torch.float64]

    def layers(self):
        from .layers import torch
        return torch


class CupyBackend(AbstractBackend):
    framework_name = 'cupy'

    def __init__(self):
        import cupy
        self.cupy = cupy

    def is_appropriate_type(self, tensor):
        return isinstance(tensor, self.cupy.ndarray)

    def from_numpy(self, x):
        return self.cupy.asarray(x)

    def to_numpy(self, x):
        return self.cupy.asnumpy(x)

    def arange(self, start, stop):

def stack_on_zeroth_dimension(self, tensors: list):
        return self.mx.symbol.stack(*tensors)

    def tile(self, x, repeats):
        return self.mx.symbol.tile(x, repeats)

    def is_float_type(self, x):
        return 'float' in str(x.infer_type()[1][0])

    def layers(self):
        from .layers import gluon
        return gluon


class TorchBackend(AbstractBackend):
    framework_name = 'torch'

    def __init__(self):
        import torch
        self.torch = torch

    def is_appropriate_type(self, tensor):
        return isinstance(tensor, self.torch.Tensor)

    def from_numpy(self, x):
        variable = self.torch.from_numpy(x)
        if self.is_float_type(variable):
            # attach grad only to floating types
            variable.requires_grad = True
        return variable

return self.tf.reshape(x, shape)

    def transpose(self, x, axes):
        return self.tf.transpose(x, axes)

    def stack_on_zeroth_dimension(self, tensors: list):
        return self.tf.stack(tensors)

    def tile(self, x, repeats):
        return self.tf.tile(x, repeats)

    def is_float_type(self, x):
        return x.dtype in ('float16', 'float32', 'float64', 'float128')


class KerasBackend(AbstractBackend):
    framework_name = 'keras'

    def __init__(self):
        import keras
        self.keras = keras
        self.K = keras.backend

    def is_appropriate_type(self, tensor):
        return self.K.is_tensor(tensor) and self.K.is_keras_tensor(tensor)

    def create_symbol(self, shape):
        return self.keras.Input(batch_shape=shape)

    def eval_symbol(self, symbol, input_dict):
        (variable, value), = input_dict
        model = self.keras.models.Model(variable, symbol)

def stack_on_zeroth_dimension(self, tensors: list):
        return self.chainer.functions.stack(tensors)

    def tile(self, x, repeats):
        return self.chainer.functions.tile(x, repeats)

    def is_float_type(self, x):
        return x.dtype in ('float16', 'float32', 'float64', 'float128')

    def layers(self):
        from .layers import chainer
        return chainer


class TensorflowBackend(AbstractBackend):
    framework_name = 'tensorflow'

    def __init__(self):
        import tensorflow
        self.tf = tensorflow

    def is_appropriate_type(self, tensor):
        return isinstance(tensor, (self.tf.Tensor, self.tf.Variable))

    def from_numpy(self, x):
        assert self.tf.executing_eagerly()
        return self.tf.convert_to_tensor(x)

    def to_numpy(self, x):
        assert self.tf.executing_eagerly()
        return x.numpy()