isReady, notReadyObjects, debugInfoObjects, err := waitForResources(objects, client.Kube(), l)
		notReady = notReadyObjects
		debugInfo = debugInfoObjects
		return isReady, err
	})

	messages := []string{}
	for _, id := range notReady {
		debug, f := debugInfo[id]
		if f {
			messages = append(messages, fmt.Sprintf("  %s (%s)", id, debug))
		} else {
			messages = append(messages, fmt.Sprintf("  %s", debug))

# RUN: not tf_tfl_translate -tf-upgrade-legacy=false -tf-input-arrays=Placeholder,Placeholder_1 -tf-input-shapes=1,2:1 -tf-output-arrays=cond/Merge -tf-enable-shape-inference-on-import=false -mlir-print-debuginfo -output-mlir %s -o - 2>&1 | FileCheck %s

# CHECK: error: The graph has Control Flow V1 ops. TFLite converter doesn't support Control Flow V1 ops. Consider using Control Flow V2 ops instead.

node {
  name: "Const"
  op: "Const"
  attr {
    key: "dtype"

# RUN: tf-mlir-translate -graphdef-to-mlir -tf-enable-shape-inference-on-import=false -tf-graph-as-function %s -o - -mlir-print-debuginfo -mlir-print-local-scope | FileCheck %s

node {
  name: "args_0"
  op: "_Arg"
  attr {
    key: "T"
    value {
      type: DT_RESOURCE
    }
  }
  attr {
    key: "index"
    value {
      i: 0
    }
  }
}
node {
  name: "args_1"
  op: "_Arg"
  attr {
    key: "T"

# RUN: tf-mlir-translate -graphdef-to-splatted-mlir %s -o - -mlir-print-debuginfo -mlir-print-local-scope | FileCheck %s

# CHECK: tf_executor._SwitchN
# CHECK-SAME: of 3 : tensor<*xi32>
# CHECK-SAME: T = i32
# CHECK-SAME: loc(fused["_SwitchN:", "Case/branch_index/_3"])
# CHECK: tf_executor._SwitchN
# CHECK-SAME: of 2 : tensor<*xf32>
# CHECK-SAME: T = f32
# CHECK-SAME: loc(fused["_SwitchN:", "Case/Case/input_0/_7"])

node {
  name: "Case/branch_index"

# Verify that TensorFlow While and StatelessWhile ops are mapped to the
# composite While op in MLIR with is_stateless attribute set accordingly to
# distinguish between them.

	if info.Size != 0 {
		ctxt.Diag("makeFuncDebugEntry double process %v", s)
	}
	var scopes []dwarf.Scope
	var inlcalls dwarf.InlCalls
	if ctxt.DebugInfo != nil {
		scopes, inlcalls = ctxt.DebugInfo(s, info, curfn)
	}
	var err error
	dwctxt := dwCtxt{ctxt}
	startPos := ctxt.InnermostPos(textPos(s))
	if !startPos.IsKnown() || startPos.RelLine() != uint(s.Func().StartLine) {

// RUN: tf-quant-opt %s -split-input-file -mlir-print-debuginfo -mlir-print-local-scope -quant-add-quantization-unit-loc -inline -quant-prepare-lifting -quant-lift-quantizable-spots-as-functions='target-opset=XLA' | FileCheck %s

// This file test the selective quantiation feature in TF Quantizer. In the test
// config, the op named "test_opt_out" will not be quantized.

module attributes {tf.versions = {bad_consumers = [], min_consumer = 12 : i32, producer = 1269 : i32}} {

// RUN: tf-quant-opt %s -split-input-file -verify-diagnostics \
// RUN:   -quant-insert-restore-op | FileCheck %s
// RUN: tf-quant-opt %s -split-input-file -verify-diagnostics \
// RUN:   -mlir-print-debuginfo -mlir-print-local-scope \
// RUN:   -quant-insert-restore-op | FileCheck %s --check-prefix CHECK-LOC

// RestoreV2 op created for a single VarHandleOp.

module attributes {tf_saved_model.semantics} {

# RUN: not tf_tfl_translate -mlir-pretty-debuginfo -tf-input-arrays=input -tf-input-data-types=DT_FLOAT -tf-input-shapes=1,224,224,3 -tf-output-arrays=MobilenetV1/MobilenetV1/Conv2d_0/BatchNorm/FusedBatchNorm -tf-debug-info=%s.debug %s -o - 2>&1 | FileCheck %s

# CHECK: fake/user/code/file_C.py:27:1: error: 'tf.Conv2D' op attribute 'data_format' failed to satisfy constraint: 'NHWC' or 'NCHW' convnet data format

node {
  name: "input"
  op: "Placeholder"
  attr {

# RUN: not tf_tfl_translate -mlir-pretty-debuginfo -tf-input-arrays=input -tf-input-data-types=DT_FLOAT -tf-input-shapes=1,224,224,3 -tf-output-arrays=MobilenetV1/MobilenetV1/Conv2d_0/BatchNorm/FusedBatchNorm -tf-debug-info=%s.debug %s -o - 2>&1 | FileCheck %s

# CHECK: fake/user/code/file_C.py:27:1: error: 'tf.Conv2D' op attribute 'data_format' failed to satisfy constraint: 'NHWC' or 'NCHW' convnet data format
# CHECK: fake/user/code/file_D.py:28:1: note: called from