Remove use of conitional pass-through from dynamic_rnn in favor of just

calling select() at each step.  conditionals add a bunch of extra ops
that slow things down; and generally the size of the input tensor into
dynamic_rnn matches max_sequence_length so they provide no benefit.

Before change, benchmarks:

Calculation: Static Unroll with Dynamic Flow LSTM vs. Dynamic Unroll LSTM
batch    max_t   units   gpu     dt(static)      dt(dynamic)     dt(dynamic)/dt(static)
256      50      512     False   1.795002        1.774248        0.988437
256      50      512     True    0.186828        0.200752        1.074525
256      50      256     False   0.597320        0.750226        1.255986
256      50      256     True    0.082047        0.091411        1.114130
256      50      128     False   0.250596        0.238233        0.950666
256      50      128     True    0.056480        0.063086        1.116960

After change, benchmarks:

Calculation: Static Unroll with Dynamic Flow LSTM vs. Dynamic Unroll LSTM                                                                                              batch    max_t   units   gpu     dt(static)      dt(dynamic)     dt(dynamic)/dt(static)
256      50      512     False   1.723348        1.763019        1.023020
256      50      512     True    0.186794        0.196334        1.051072
256      50      256     False   0.644540        0.704506        1.093036
256      50      256     True    0.082274        0.087785        1.066985
256      50      128     False   0.241971        0.234559        0.969368
256      50      128     True    0.056356        0.059771        1.060611

Basically expect a more significant decrease in GPU step time when the matrices are smaller.
Change: 117254684

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