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Compute a recurrent neural net.

Roughly, Recurrent() computes the following: state = state0 for t in inputs' sequence length: state = cell_fn(theta, state, inputs[t, :]) accumulate_state[t, :] = state return accumulate_state, state

theta, state, inputs are all structures of tensors.

inputs[t, :] means taking a slice out from every tensor in the inputs.

accumulate_state[t, :] = state means that we stash every tensor in 'state' into a slice of the corresponding tensor in accumulate_state.

cell_fn is a python callable computing (building up a TensorFlow graph) the recurrent neural network's one forward step. Two calls of cell_fn must describe two identical computations.

By construction, Recurrent()'s backward computation does not access any intermediate values computed by cell_fn during forward computation. We may extend Recurrent() to support that by taking a customized backward function of cell_fn.

theta weights. A structure of tensors.
state0 initial state. A structure of tensors.
inputs inputs. A structure of tensors.
cell_fn A python function, which computes: state1, extras = cell_fn(theta, state0, inputs[t, :])
cell_grad A python function which computes: dtheta, dstate0, dinputs[t, :] = cell_grad( theta, state0, inputs[t, :], extras, dstate1)
extras A structure of tensors. The 2nd return value of every invocation of cell_fn is a structure of tensors with matching keys and shapes of this extras.
max_input_length maximum length of effective input. This is used to truncate the computation if the inputs have been allocated to a larger size. A scalar tensor.
use_tpu whether or not we are on TPU.
aligned_end A boolean indicating whether the sequence is aligned at the end.

accumulate_state and the final state.