// been converted to constants, all the assign ops are gone, etc.). However,
// TFLite has these variable tensors semantics. So the variable mapping from TF
// to TFLite is actually broken here, we sort of hard-code the variable tensors
// based on the actual ops using them, such as unidirectional_sequence_lstm.
//
// MLIRConverter also benefits from lots of typical compiler optimization like

    return %result : tensor<1x1xf32>
  }
}

// -----
// Variable tensor for host can still be used.
//
// CHECK-LABEL:  func.func @serving_default(%arg0: tensor<1x3xf32>) -> (tensor<1x1xf32>, tensor<1x1xf32>) {
// CHECK:  "tf.VarHandleOp"
// CHECK-NOT:  [[VARIABLE:%.*]] = "tf.ReadVariableOp"
// CHECK-NEXT:  [[KEY:%.*]], [[FUTURE:%.*]] = "tf.IfrtLoadVariable"

        checkArgument(!variable.equalsType(type), "Type variable %s bound to itself", variable);
        builder.put(variable, type);
      }
      return new TypeTable(builder.buildOrThrow());
    }

    final Type resolve(TypeVariable<?> var) {
      TypeTable unguarded = this;
      TypeTable guarded =
          new TypeTable() {
            @Override

        for range s {}
        for i = range s {}
        for i, j = range s {} // ERROR "range over .* permits only one iteration variable"
        for i, j, k = range s {} // ERROR "range over .* permits only one iteration variable"
        for i, j, k, l = range s {} // ERROR "range over .* permits only one iteration variable"

		// If lhs exists, declare a corresponding variable in the case-local scope.
		if lhs != nil {
			// spec: "The TypeSwitchGuard may include a short variable declaration.
			// When that form is used, the variable is declared at the beginning of
			// the implicit block in each clause. In clauses with a case listing
			// exactly one type, the variable has that type; otherwise, the variable

}

// Looks up the variable handle that provides input to node with node_name,
// and returns the handle name if the handle corresponds to a variable that we
// want to freeze (i.e. its name is contained in variable_node_names). If there
// is no such handle in the graph (or we do not want to save that variable)
// then NotFound error is returned.
StatusOr<string> GetHandleNameIfNeedsToFreeze(

// contains maps keyed by the node number of the SavedObjectGraph, and map to an
// object of the corresponding type. So, if node 2 in the object graph is a
// variable, PartiallyRevivedObjects.variables[2] exists, and corresponds to a
// tensorflow::Variable object. The only exception to this is the
// "signatures_map", which is keyed by the "signature" key

}

// stackBufAddr returns the expression &tmp, where tmp is a newly
// allocated temporary variable of type [len]elem. This variable is
// initialized, and elem must not contain pointers.
func stackBufAddr(len int64, elem *types.Type) *ir.AddrExpr {
	if elem.HasPointers() {
		base.FatalfAt(base.Pos, "%v has pointers", elem)
	}
	tmp := typecheck.TempAt(base.Pos, ir.CurFunc, types.NewArray(elem, len))

variable "TAGS" {
  default = "latest"
}

variable "HUB" {
  default = "localhost:5000"
}

variable "PLATFORMS" {
  default = "linux/amd64,linux/arm64"
}

images = [
  // Productpage
  {
    name   = "examples-bookinfo-productpage-v1"
    source = "productpage"
  },
  {
    name = "examples-bookinfo-productpage-v-flooding"
    args = {
      flood_factor = 100
    }
    source = "productpage"
  },

		_ = mi
	}
}

func _[T int | string](x T) {
	for range x /* ERROR "cannot range over x (variable of type T constrained by int | string): no core type" */ {
	}
}

func _[T int | int64](x T) {
	for range x /* ERROR "cannot range over x (variable of type T constrained by int | int64): no core type" */ {
	}
}

func _[T ~int](x T) {
	for range x { // ok
	}
}

func issue65133() {