91.666667%;\n}\n\n.col-12 {\n  flex: 0 0 100%;\n  max-width: 100%;\n}\n\n.order-first {\n  order: -1;\n}\n\n.order-last {\n  order: 13;\n}\n\n.order-0 {\n  order: 0;\n}\n\n.order-1 {\n  order: 1;\n}\n\n.order-2 {\n  order: 2;\n}\n\n.order-3 {\n  order: 3;\n}\n\n.order-4 {\n  order: 4;\n}\n\n.order-5 {\n  order: 5;\n}\n\n.order-6 {\n  order: 6;\n}\n\n.order-7 {\n  order: 7;\n}\n\n.order-8 {\n  order: 8;\n}\n\n.order-9 {\n  order: 9;\n}\n\n.order-10 {\n  order: 10;\n}\n\n.order-11 {\n  order: 11;\n}\n\n.order-12 {\n...

91.666667%;\n}\n\n.col-12 {\n  flex: 0 0 100%;\n  max-width: 100%;\n}\n\n.order-first {\n  order: -1;\n}\n\n.order-last {\n  order: 13;\n}\n\n.order-0 {\n  order: 0;\n}\n\n.order-1 {\n  order: 1;\n}\n\n.order-2 {\n  order: 2;\n}\n\n.order-3 {\n  order: 3;\n}\n\n.order-4 {\n  order: 4;\n}\n\n.order-5 {\n  order: 5;\n}\n\n.order-6 {\n  order: 6;\n}\n\n.order-7 {\n  order: 7;\n}\n\n.order-8 {\n  order: 8;\n}\n\n.order-9 {\n  order: 9;\n}\n\n.order-10 {\n  order: 10;\n}\n\n.order-11 {\n  order: 11;\n}\n\n.order-12 {\n...

                                    type: object
                                  type: array
                                failoverPriority:
                                  description: failoverPriority is an ordered list
                                    of labels used to sort endpoints to do priority
                                    based load balancing.
                                  items:

                                    type: object
                                  type: array
                                failoverPriority:
                                  description: failoverPriority is an ordered list
                                    of labels used to sort endpoints to do priority
                                    based load balancing.
                                  items:

col-12{-ms-flex:0 0 100%;flex:0 0 100%;max-width:100%}.order-first{-ms-flex-order:-1;order:-1}.order-last{-ms-flex-order:13;order:13}.order-0{-ms-flex-order:0;order:0}.order-1{-ms-flex-order:1;order:1}.order-2{-ms-flex-order:2;order:2}.order-3{-ms-flex-order:3;order:3}.order-4{-ms-flex-order:4;order:4}.order-5{-ms-flex-order:5;order:5}.order-6{-ms-flex-order:6;order:6}.order-7{-ms-flex-order:7;order:7}.order-8{-ms-flex-order:8;order:8}.order-9{-ms-flex-order:9;order:9}.order-10{-ms-flex-order:10...

and intense, are most probably of the second order. Those of the first
and third order also may be pretty good; only the yellow of the first
order is faint, and the orange and red of the third Order have a great
Mixture of violet and blue.

There may be good _Greens_ of the fourth Order, but the purest are of
the third. And of this Order the green of all Vegetables seems to be,

                                    type: object
                                  type: array
                                failoverPriority:
                                  description: failoverPriority is an ordered list
                                    of labels used to sort endpoints to do priority
                                    based load balancing.
                                  items:

input format is recommended for optimal behavior:

if adjoint_a == false:
  A should be sorted in lexicographically increasing order.  Use SparseReorder
  if you're not sure.
if adjoint_a == true:
  A should be sorted in order of increasing dimension 1 (i.e., "column major"
  order instead of "row major" order).
  }];

  let arguments = (ins

                  non-empty, then the implementation MUST accept exactly one of these two
                  routes, determined by the following criteria, in order:


                  * The oldest Route based on creation timestamp.
                  * The Route appearing first in alphabetical order by
                    "{namespace}/{name}".


                  The rejected Route MUST raise an 'Accepted' condition with a status of

semantics to match NumPy. * `tf.split` now takes arguments in a reversed order
and with different keywords. In particular, we now match NumPy order as
`tf.split(value, num_or_size_splits, axis)`. * `tf.sparse_split` now takes
arguments in reversed order and with different keywords. In particular we now
match NumPy order as `tf.sparse_split(sp_input, num_split, axis)`. NOTE: we have