def METHOD_NAME(path, strength):
    sd = comfy.utils.load_torch_file(path, safe_load=True)
    activation_func = sd.get('activation_func', 'linear')
    is_layer_norm = sd.get('is_layer_norm', False)
    use_dropout = sd.get('use_dropout', False)
    activate_output = sd.get('activate_output', False)
    last_layer_dropout = sd.get('last_layer_dropout', False)
    valid_activation = {
        "linear": torch.nn.Identity,
        "relu": torch.nn.ReLU,
        "leakyrelu": torch.nn.LeakyReLU,
        "elu": torch.nn.ELU,
        "swish": torch.nn.Hardswish,
        "tanh": torch.nn.Tanh,
        "sigmoid": torch.nn.Sigmoid,
        "softsign": torch.nn.Softsign,
    }
    if activation_func not in valid_activation:
        print("Unsupported Hypernetwork format, if you report it I might implement it.", path, " ", activation_func, is_layer_norm, use_dropout, activate_output, last_layer_dropout)
        return None
    out = {}
    for d in sd:
        try:
            dim = int(d)
        except:
            continue
        output = []
        for index in [0, 1]:
            attn_weights = sd[dim][index]
            keys = attn_weights.keys()
            linears = filter(lambda a: a.endswith(".weight"), keys)
            linears = list(map(lambda a: a[:-len(".weight")], linears))
            layers = []
            for i in range(len(linears)):
                lin_name = linears[i]
                last_layer = (i == (len(linears) - 1))
                penultimate_layer = (i == (len(linears) - 2))
                lin_weight = attn_weights['{}.weight'.format(lin_name)]
                lin_bias = attn_weights['{}.bias'.format(lin_name)]
                layer = torch.nn.Linear(lin_weight.shape[1], lin_weight.shape[0])
                layer.load_state_dict({"weight": lin_weight, "bias": lin_bias})
                layers.append(layer)
                if activation_func != "linear":
                    if (not last_layer) or (activate_output):
                        layers.append(valid_activation[activation_func]())
                if is_layer_norm:
                    layers.append(torch.nn.LayerNorm(lin_weight.shape[0]))
                if use_dropout:
                    if (not last_layer) and (not penultimate_layer or last_layer_dropout):
                        layers.append(torch.nn.Dropout(p=0.3))
            output.append(torch.nn.Sequential(*layers))
        out[dim] = torch.nn.ModuleList(output)
    class hypernetwork_patch:
        def __init__(self, hypernet, strength):
            self.hypernet = hypernet
            self.strength = strength
        def __call__(self, q, k, v, extra_options):
            dim = k.shape[-1]
            if dim in self.hypernet:
                hn = self.hypernet[dim]
                k = k + hn[0](k) * self.strength
                v = v + hn[1](v) * self.strength
            return q, k, v
        def to(self, device):
            for d in self.hypernet.keys():
                self.hypernet[d] = self.hypernet[d].to(device)
            return self
    return hypernetwork_patch(out, strength)