def _keras_dense(converter, interiors, inputs):
    x_in = converter.outputs[inputs[0]]
    kernel = interiors['kernel']
    k = _nodef_to_private_pond(converter, kernel)
    try:
        bias = interiors['bias']
        b = _nodef_to_private_pond(converter, bias)
    except KeyError:
        b = None
    input_shape = x_in.shape.as_list()
    shape = [i.size for i in kernel.attr['value'].tensor.tensor_shape.dim]
    layer = Dense(input_shape, out_features=shape[1])
    layer.initialize(initial_weights=k, initial_bias=b)
    out = layer.forward(x_in)
    return out
def _keras_dense(converter, interiors, inputs):
    x_in = converter.outputs[inputs[0]]
    kernel = interiors['kernel']
    k = _nodef_to_private_pond(converter, kernel)
    try:
        bias = interiors['bias']
        b = _nodef_to_private_pond(converter, bias)
    except KeyError:
        b = None
    input_shape = x_in.shape.as_list()
    shape = [i.size for i in kernel.attr['value'].tensor.tensor_shape.dim]
    layer = Dense(input_shape, out_features=shape[1])
    layer.initialize(initial_weights=k, initial_bias=b)
    out = layer.forward(x_in)
    return out