import org.opensearch.action.admin.indices.alias.Alias;
import org.opensearch.action.admin.indices.alias.IndicesAliasesRequestBuilder;
import org.opensearch.action.admin.indices.alias.get.GetAliasesResponse;
import org.opensearch.action.admin.indices.create.CreateIndexResponse;
import org.opensearch.action.admin.indices.exists.indices.IndicesExistsResponse;
import org.opensearch.action.admin.indices.get.GetIndexResponse;

	_ = array[3:1:2] // ERROR "invalid slice index|invalid slice indices|inverted slice"
	_ = array[3:2:1] // ERROR "invalid slice index|invalid slice indices|inverted slice"
	_ = array[i:1:2]
	_ = array[i:2:1] // ERROR "invalid slice index|invalid slice indices|inverted slice"
	_ = array[1:j:2]
	_ = array[2:j:1] // ERROR "invalid slice index|invalid slice indices"
	_ = array[1:2:k]

  // merge these 1s (using axes for indices) to the correct
  // position in the result.
  // axes_ones = [1, 1]
  auto axes_ones = OnesLike(scope, axes);

  // using DynamicStitch:
  // indices = { input_rank_range, axes }
  //         = { [0, 1, 2, 3], [1, 2] }
  // data = { input_shape, axes_ones }
  //      = { [2, 3, 5, 7], [1, 1] }
  // The input_rank_range entry in indices first replicates the

  %0 = "tfl.pseudo_sparse_const"() {compressed_data = dense<[1.0, 2.0, 0.5, 0.25,...

	indices, err := c.allocate(ctx, model)
	return len(indices) == len(c.requests), err
}

func (c *Controller) Allocate(ctx context.Context, model Model) ([]*resourceapi.NamedResourcesAllocationResult, error) {
	indices, err := c.allocate(ctx, model)
	if err != nil {
		return nil, err
	}
	if len(indices) != len(c.requests) {
		return nil, errors.New("insufficient resources")

import org.codelibs.fess.suggest.util.SuggestUtil;
import org.opensearch.action.admin.indices.analyze.AnalyzeAction;
import org.opensearch.action.admin.indices.analyze.AnalyzeAction.AnalyzeToken;
import org.opensearch.action.admin.indices.exists.indices.IndicesExistsResponse;
import org.opensearch.action.admin.indices.settings.get.GetSettingsResponse;
import org.opensearch.action.search.SearchResponse;

  %0 = "tfl.pseudo_sparse_const"() {compressed_data = dense<[1.0, 2.0, 0.5, 0.25,...

//
// Eg 1. An update in canonical form:
//  * indices shape(A,B,C)
//  * updates shape(A,B,D,E,F)
// Then:
//  * D,E,F are the update window dims [2,3,4]
//  * C is the index vector dimension
//  * A,B iterate over the updates and indices
//
// If `update_window_dims` are not the trailing dimensions then updates must be
// transposed.
//
// Eg 2. An update in non-canonical form:
//  * indices shape(a,b,c)
//  * updates shape(d,e,f,a,b)

  let summary = "get a tensor in resource array";

  let description = [{
    Get a tensor in resource array.

    indices: the indices in the resource array.
    results: the tensor values for the corresponding indices.
  }];

  let arguments = (ins
    I64ArrayAttr:$indices
  );

  let results = (outs
    Variadic<TFTensorType>:$results
  );

  let assemblyFormat = "attr-dict `:` type($results)";

	case *ast.IndexExpr:
		return &IndexExpr{
			Orig:    e,
			X:       e.X,
			Lbrack:  e.Lbrack,
			Indices: []ast.Expr{e.Index},
			Rbrack:  e.Rbrack,
		}
	case *ast.IndexListExpr:
		return &IndexExpr{
			Orig:    e,
			X:       e.X,
			Lbrack:  e.Lbrack,
			Indices: e.Indices,
			Rbrack:  e.Rbrack,
		}
	}
	return nil