padding-right: 15px;\n  padding-left: 15px;\n}\n\n.col {\n  flex-basis: 0;\n  flex-grow: 1;\n  max-width: 100%;\n}\n\n.row-cols-1 > * {\n  flex: 0 0 100%;\n  max-width: 100%;\n}\n\n.row-cols-2 > * {\n  flex: 0 0 50%;\n  max-width: 50%;\n}\n\n.row-cols-3 > * {\n  flex: 0 0 33.333333%;\n  max-width: 33.333333%;\n}\n\n.row-cols-4 > * {\n  flex: 0 0 25%;\n  max-width: 25%;\n}\n\n.row-cols-5 > * {\n  flex: 0 0 20%;\n  max-width: 20%;\n}\n\n.row-cols-6 > * {\n  flex: 0 0 16.666667%;\n  max-width: 16.666667%;\n}\n\n.col-auto...

-3 {\n  gap: 1rem !important;\n}\n\n.gap-4 {\n  gap: 1.5rem !important;\n}\n\n.gap-5 {\n  gap: 3rem !important;\n}\n\n.row-gap-0 {\n  row-gap: 0 !important;\n}\n\n.row-gap-1 {\n  row-gap: 0.25rem !important;\n}\n\n.row-gap-2 {\n  row-gap: 0.5rem !important;\n}\n\n.row-gap-3 {\n  row-gap: 1rem !important;\n}\n\n.row-gap-4 {\n  row-gap: 1.5rem !important;\n}\n\n.row-gap-5 {\n  row-gap: 3rem !important;\n}\n\n.column-gap-0 {\n  -moz-column-gap: 0 !important;\n  column-gap: 0 !important;\n}\n\n.column-gap-1...

pkg path/filepath, func IsLocal(string) bool #56219
pkg path/filepath, var SkipAll error #47209
pkg reflect, method (Value) Comparable() bool #46746
pkg reflect, method (Value) Equal(Value) bool #46746
pkg reflect, method (Value) Grow(int) #48000
pkg reflect, method (Value) SetZero() #52376
pkg regexp/syntax, const ErrLarge ErrorCode #56041
pkg regexp/syntax, const ErrLarge = "expression too large" #56041

don't // need constant-time lookups or constant-time digit // computations. // // So we use a non-adjacent form of some width w instead of // radix 16. This is like a binary representation (one digit // for each binary place) but we allow the digits to grow in // magnitude up to 2^{w-1} so that the nonzero digits are as // sparse as possible. Intuitively, this "condenses" the // "mass" of the scalar onto sparse coefficients (meaning // fewer additions). basepointNafTable := basepointNafTable() var aTable...

    *   TPUs can now be re-initialized multiple times, using
        `tf.tpu.experimental.initialize_tpu_system`.
    *   Add `RaggedTensor.merge_dims()`.
    *   Added new `uniform_row_length` row-partitioning tensor to
        `RaggedTensor`.
    *   Add `shape` arg to `RaggedTensor.to_tensor`; Improve speed of
        `RaggedTensor.to_tensor`.