// first and subsequent calls for a block.  For the first call, the
// starting state is approximated by taking the state from the
// predecessor whose state is smallest, and removing any elements not
// in all the other predecessors; this makes the smallest number of
// changes and shares the most state.  On subsequent calls the old
// value of startState is adjusted with new information; this is judged

			// than 1000 pages (which would be result of talking to etcd with page size
			// copied from pred.Limit).
			// The expected number of calls is n+1 where n is the smallest n so that:
			// pageSize + pageSize * 2 + pageSize * 4 + ... + pageSize * 2^n >= podCount.
			// For pageSize = 1, podCount = 1000, we get n+1 = 10, 2 ^ 10 = 1024.
			currLimit := pageSize

 *       scalability or other performance factors (see the separate "jtreg" tests for a set intended
 *       to check these for the most central aspects of functionality.) So, most tests use the
 *       smallest sensible numbers of threads, collection sizes, etc needed to check basic
 *       conformance.
 *   <li>The test classes currently do not declare inclusion in any particular package to simplify

	}
	if resourceVersion < oldest-1 {
		return nil, errors.NewResourceExpired(fmt.Sprintf("too old resource version: %d (%d)", resourceVersion, oldest-1))
	}

	// Binary search the smallest index at which resourceVersion is greater than the given one.
	f := func(i int) bool {
		return w.cache[(w.startIndex+i)%w.capacity].ResourceVersion > resourceVersion
	}
	first := sort.Search(size, f)

  TF_DerivedResultTypeAttr output_type = TF_DerivedResultTypeAttr<0>;
}

def TF_ArgMinOp : TF_Op<"ArgMin", [Pure]> {
  let summary = [{
Returns the index with the smallest value across dimensions of a tensor.
  }];

  let description = [{
Note that in case of ties the identity of the return value is not guaranteed.

Usage:
  ```python
  import tensorflow as tf

		if !elem.IsArray() {
			s.Fatalf("expected array type: %v", elem)
		}
		elem, rtypeExpr = elem.Elem(), n.ElemElemRType
	}
	size := elem.Size()
	// Casting from larger type to smaller one is ok, so for smallest type, do nothing.
	if elem.Alignment() == 1 && (size == 0 || size == 1 || count == nil) {
		return
	}
	if count == nil {
		count = s.constInt(types.Types[types.TUINTPTR], 1)
	}

    *   [Improve support](https://github.com/tensorflow/tensorflow/issues/33862)
        for dynamically-sized TensorArray inside `tf.function`.
*   `tf.math`:
    *   Narrow down `argmin`/`argmax` contract to always return the smallest
        index for ties.
    *   `tf.math.reduce_variance` and `tf.math.reduce_std` return correct
        computation for complex types and no longer support integer types.