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Adds a bias to the inputs.

Can be used as a normalizer function for conv2d and fully_connected.

inputs A tensor of with at least rank 2 and value for the last dimension, e.g. [batch_size, depth], [None, None, None, depth].
activation_fn Activation function, default set to None to skip it and maintain a linear activation.
initializer An initializer for the bias, defaults to 0.
regularizer A regularizer like the result of l1_regularizer or l2_regularizer.
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).
data_format A string. 'NHWC' and 'NCHW' are supported.
scope Optional scope for variable_scope.

A tensor representing the result of adding biases to the inputs.

ValueError If data_format is neither NHWC nor NCHW.
ValueError If data_format is NCHW and rank of inputs is not 4.
ValueError If the rank of inputs is undefined.
ValueError If rank or C dimension of inputs is undefined.