function basicLSTMCell_(forgetBias, lstmKernel, lstmBias, data, c, h) {
    const $forgetBias = (0, _tensorUtilEnv.convertToTensor)(forgetBias, "forgetBias", "basicLSTMCell");
    const $lstmKernel = (0, _tensorUtilEnv.convertToTensor)(lstmKernel, "lstmKernel", "basicLSTMCell");
    const $lstmBias = (0, _tensorUtilEnv.convertToTensor)(lstmBias, "lstmBias", "basicLSTMCell");
    const $data = (0, _tensorUtilEnv.convertToTensor)(data, "data", "basicLSTMCell");
    const $c = (0, _tensorUtilEnv.convertToTensor)(c, "c", "basicLSTMCell");
    const $h = (0, _tensorUtilEnv.convertToTensor)(h, "h", "basicLSTMCell");
    const combined = (0, _concat.concat)([
        $data,
        $h
    ], 1);
    const weighted = (0, _matMul.matMul)(combined, $lstmKernel);
    const res = (0, _add.add)(weighted, $lstmBias);
    // i = input_gate, j = new_input, f = forget_gate, o = output_gate
    const batchSize = res.shape[0];
    const sliceCols = res.shape[1] / 4;
    const sliceSize = [
        batchSize,
        sliceCols
    ];
    const i = (0, _slice.slice)(res, [
        0,
        0
    ], sliceSize);
    const j = (0, _slice.slice)(res, [
        0,
        sliceCols
    ], sliceSize);
    const f = (0, _slice.slice)(res, [
        0,
        sliceCols * 2
    ], sliceSize);
    const o = (0, _slice.slice)(res, [
        0,
        sliceCols * 3
    ], sliceSize);
    const newC = (0, _add.add)((0, _mul.mul)((0, _sigmoid.sigmoid)(i), (0, _tanh.tanh)(j)), (0, _mul.mul)($c, (0, _sigmoid.sigmoid)((0, _add.add)($forgetBias, f))));
    const newH = (0, _mul.mul)((0, _tanh.tanh)(newC), (0, _sigmoid.sigmoid)(o));
    return [
        newC,
        newH
    ];
}
Computes the next state and output of a BasicLSTMCell.

Returns `[newC, newH]`.

Derived from tf.contrib.rnn.BasicLSTMCell.

@param forgetBias Forget bias for the cell.
@param lstmKernel The weights for the cell.
@param lstmBias The bias for the cell.
@param data The input to the cell.
@param c Previous cell state.
@param h Previous cell output.

@doc {heading: 'Operations', subheading: 'RNN'}