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# TensorFlow contrib layers.
## initializers.py
Functions that produce variable initializer functions with signature:
`foo(shape, dtype) : Tensor`
These are typically consumed by functions in [layers.py](#layers.py).
## layers.py {#.py}
Functions that produce layer operations and associated weight & bias variables. Signatures will vary for different functions, but they will often take many of
these arguments.
`foo(x,
num_outputs,
…,
weight_init=<DEFAULT>,
bias_init=<DEFAULT>,
weight_regularizer=None,
bias_regularizer=None,
name=None) : Tensor`
`x` is the input tensor.
Weights and biases are added to `tf.GraphKeys.GLOBAL_VARIABLES` and
`tf.GraphKeys.TRAINABLE_VARIABLES`.
## optimizers.py
Functions that add optimization ops given `loss` and `global_step` tensors.
## regularizers.py
Functions that produce weight regularization functions with signature
`foo(weight_vars, name=None) : Operation`
These are typically consumed by functions in [layers.py](#layers.py).
## summaries.py
Functions that add summary ops to the standard `tf.GraphKeys.SUMMARIES`
collection. They also avoid name conflicts in the summary key.
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