human
Solve the following python programming problem given the following code:
@slim.add_arg_scope
def weighted_instance_norm(inputs, weights, num_categories, center=True, scale=True, activation_fn=None, reuse=None, variables_collections=None, outputs_collections=None, trainable=True, scope=None):
assistant
#Here is the Python code you requested.

    """Weighted instance normalization.
    Can be used as a normalizer function for conv2d.
    Args:
    inputs: a tensor with 4 dimensions. The normalization occurs over height
    and width.
    weights: 1D tensor.
    num_categories: int, total number of styles being modeled.
    center: If True, subtract `beta`. If False, `beta` is ignored.
    scale: If True, multiply by `gamma`. If False, `gamma` is
    not used. When the next layer is linear (also e.g. `nn.relu`), this can be
    disabled since the scaling can be done by the next layer.
    activation_fn: Optional activation function.
    reuse: whether or not the layer and its variables should be reused. To be
    able to reuse the layer scope must be given.
    variables_collections: optional collections for the variables.
    outputs_collections: collections to add the outputs.
    trainable: If `True` also add variables to the graph collection
    `GraphKeys.TRAINABLE_VARIABLES` (see tf.Variable).
    scope: Optional scope for `variable_scope`.
    Returns:
    A `Tensor` representing the output of the operation.
    Raises:
    ValueError: if rank or last dimension of `inputs` is undefined, or if the
    input doesn\t have 4 dimensions.
    """
    with tf.variable_scope(scope, 'InstanceNorm', [inputs], reuse=reuse) as sc:
        inputs = tf.convert_to_tensor(inputs)
        inputs_shape = inputs.get_shape()
        inputs_rank = inputs_shape.ndims
        if (inputs_rank is None):
            raise ValueError(('Inputs    %s    has    undefined    rank.' % inputs.name))
        if (inputs_rank != 4):
            raise ValueError(('Inputs    %s    is    not    a    4D    tensor.' % inputs.name))
        dtype = inputs.dtype.base_dtype
        axis = [1, 2]
        params_shape = inputs_shape[(-1):]
        if (not params_shape.is_fully_defined()):
            raise ValueError(('Inputs    %s    has    undefined    last    dimension    %s.' % (inputs.name, params_shape)))
        def _weighted_variable(name, initializer, weights, num_categories):
            'Weighting.'
            shape = tf.TensorShape([num_categories]).concatenate(params_shape)
            var_collections = slim.utils.get_variable_collections(variables_collections, name)
            var = slim.model_variable(name, shape=shape, dtype=dtype, initializer=initializer, collections=var_collections, trainable=trainable)
            weights = tf.reshape(weights, weights.get_shape().concatenate(([1] * params_shape.ndims)))
            conditioned_var = (weights * var)
            conditioned_var = tf.reduce_sum(conditioned_var, 0, keep_dims=True)
            conditioned_var = tf.expand_dims(tf.expand_dims(conditioned_var, 1), 1)
            return conditioned_var
        (beta, gamma) = (None, None)
        if center:
            beta = _weighted_variable('beta', tf.zeros_initializer(), weights, num_categories)
        if scale:
            gamma = _weighted_variable('gamma', tf.ones_initializer(), weights, num_categories)
        (mean, variance) = tf.nn.moments(inputs, axis, keep_dims=True)
        variance_epsilon = 1e-05
        outputs = tf.nn.batch_normalization(inputs, mean, variance, beta, gamma, variance_epsilon)
        outputs.set_shape(inputs_shape)
        if activation_fn:
  ...