 /// segments.
 ///
 /// Computes a tensor such that
-/// `(output[i] = sum_{j...} data[j...]` where the sum is over tuples `j...` such
+/// \\(output[i] = sum_{j...} data[j...]\\) where the sum is over tuples `j...` such
 /// that `segment_ids[j...] == i`.  Unlike `SegmentSum`, `segment_ids`
 /// need not be sorted and need not cover all values in the full
 /// range of valid values.

/// Quantizes then dequantizes a tensor.
///
/// This op simulates the precision loss from the quantized forward pass by:
/// 1. Quantizing the tensor to fixed point numbers, which should match the target
///    quantization method when it is used in inference.
/// 2. Dequantizing it back to floating point numbers for the following ops, most

///
/// `updates` is `Tensor` of rank `Q-1+P-K` with shape:
///
/// $$[d_0, ..., d_{Q-2}, ref.shape[K], ..., ref.shape[P-1]].$$
///
/// For example, say we want to update 4 scattered elements to a rank-1 tensor to
/// 8 elements. In Python, that update would look like this:
