// RUN: tfr-opt %s -verify-diagnostics -split-input-file | tfr-opt | FileCheck %s

// Tests for types, ops with custom constraints, verifiers, printer or parser
// methods.

// CHECK-LABEL: tensor_type_noconstraint
func.func private @tensor_type_noconstraint() -> !tfr.tensor

// -----

// CHECK-LABEL: tensor_type
func.func private @tensor_type() -> !tfr.tensor<T>

// -----

// CHECK-LABEL: tensor_list_type_noconstraint

shared state, and the operations that mutate that state. It maps the nodes of a dataflow graph across many machines in a cluster, and within a machine across multiple computational devices, including multicore CPUs, general purpose GPUs, and custom-designed ASICs known as Tensor Processing Units (TPUs). This architecture gives flexibility to the application developer, whereas in previous “parameter server” designs the management of shared state is built into the system, TensorFlow enables developers to...

	policy := &nonePolicy{}

	st := &mockState{
		assignments:   state.ContainerCPUAssignments{},
		defaultCPUSet: cpuset.New(cpuIDs...),
	}

	cpus := policy.GetAllocatableCPUs(st)
	if cpus.Size() != 0 {
		t.Errorf("NonePolicy GetAllocatableCPUs() error. expected empty set, returned: %v", cpus)
	}
}

func TestNonePolicyOptions(t *testing.T) {
	var err error

	_, err = NewNonePolicy(nil)
	if err != nil {

  Scope on_cpu = root.WithAssignedDevice(kCPU).WithDevice(kCPU);

  Output in0 = ops::Placeholder(on_gpu.WithOpName("in0"), DT_FLOAT);
  Output in1 = ops::Placeholder(on_gpu.WithOpName("in1"), DT_FLOAT);

  Output perm = ops::Const(on_cpu.WithOpName("perm"), {3, 1, 2, 0});

  {
    Output tr0 = ops::Transpose(on_gpu.WithOpName("tr0"), in0, perm);
    Output tr1 = ops::Transpose(on_gpu.WithOpName("tr1"), in1, perm);
  }

  let description = [{
    Interface to verify TFLite runtime op verification.

    This verifies that the converted TFLite ops has operand/result type
    supported by the TFLite runtime.
  }];

  let methods = [
    StaticInterfaceMethod<
      [{Returns whether the op's operands/results are supported by runtime.}],
      "LogicalResult", "VerifyTflRuntimeConstraints",

      auto y = Identity(scope_, x);
      RunTest(x, shape, y, shape);
    }
    ```

NOTE: There are some ops that require features from the C++ API that are not yet
implemented.

*   Ops that require PartialTensorShape information cannot yet be implemented.

*   Ops that require SparseTensor or IndexSlices (currently only in python)
    cannot yet be implemented.

*   Maybe more.

Tres Popp <******@****.***> 1648211118 -0700

	opts := validation.ValidationOptionsForNetworking(obj.(*networking.NetworkPolicy), old.(*networking.NetworkPolicy))
	validationErrorList := validation.ValidateNetworkPolicy(obj.(*networking.NetworkPolicy), opts)
	updateErrorList := validation.ValidateNetworkPolicyUpdate(obj.(*networking.NetworkPolicy), old.(*networking.NetworkPolicy), opts)
	return append(validationErrorList, updateErrorList...)
}

		}
	}
	uaAppend("; ", "")

	if cpus, err := gopsutilcpu.Info(); err == nil && len(cpus) > 0 {
		cpuMap := make(map[string]struct{}, len(cpus))
		coreMap := make(map[string]struct{}, len(cpus))
		for i := range cpus {
			cpuMap[cpus[i].PhysicalID] = struct{}{}
			coreMap[cpus[i].CoreID] = struct{}{}
		}
		cpu := cpus[0]

// RUN: tf-opt %s -split-input-file -verify-diagnostics | FileCheck %s

// Tests for TensorFlow ops with custom verifiers.

// TODO(hinsu): Remove tests for ops without custom verifiers. These tests were
// added along with manual op definition and are obsolete now that the op
// definitions are auto-generated.

// TODO(hinsu): Move attribute and type tests to types.mlir file.
//===--------------------------------------------------------------------===//