std::vector<int64_t> rhs_shape = rhs_type.getShape();

  Location loc = op.getLoc();

  // Ensure that input ranks are at least 2.
  const int lhs_dims = lhs_shape.size();
  const int rhs_dims = rhs_shape.size();
  if (lhs_dims < 2 || rhs_dims < 2) {
    // Both inputs must have rank >= 2
    return failure();
  }

  // Replace the last 2 dimensions of LHS and RHS if necessary.

            task myTask {
                def inputFile = file("input.txt")
                def outputFile = file("output.txt")
                inputs.file(inputFile)
                outputs.file(outputFile)
                doLast {
                    outputFile.text = inputFile.text
                }
            }
        """
        file("input.txt").text = "input"

        when:
        run "myTask", "--debug"

    // Fetch inputs from the OpKernelContext. Inputs are the same as the ones
    // for XlaCompile, except that the must-be-constant inputs that appear in
    // the beginning are stripped off and the closure key is appended as the
    // last input. So the inputs look like: input tensors, resource variables,
    // closure key tensor.
    std::vector<const Tensor*> inputs = InputsFromContext(ctx);

      TF_AddGradientsWithPrefix(graph_, prefix, outputs, noutputs, inputs,
                                ninputs, grad_inputs, s_, grad_outputs);
    } else {
      TF_AddGradientsWithPrefix(graph_, prefix, outputs, noutputs, inputs,
                                ninputs, nullptr, s_, grad_outputs);
    }
  }

  void BuildErrorGraph(TF_Output* inputs, TF_Output* outputs) {

	"k8s.io/apiserver/pkg/admission/plugin/cel"
	"k8s.io/apiserver/pkg/authorization/authorizer"
)

var _ cel.ExpressionAccessor = &ValidationCondition{}

// ValidationCondition contains the inputs needed to compile, evaluate and validate a cel expression
type ValidationCondition struct {
	Expression string
	Message    string
	Reason     *metav1.StatusReason
}

//RUN: tf_tfl_translate --enable-stablehlo-conversion --input-mlir %s -o - | flatbuffer_translate --tflite-flatbuffer-to-mlir - -o - | FileCheck %s


module attributes {tf.versions = {bad_consumers = [], min_consumer = 0 : i32, producer = 1660 : i32}} {
  func.func @main(%arg0: tensor<2x3xi32>) -> tensor<2x3xi32> attributes {tf.entry_function = {control_outputs = "", inputs = "args_tf_0", outputs = "Identity"}} {
    %0 = tf_executor.graph {

  std::vector<const Tensor*> inputs;
  inputs.push_back(a);
  inputs.push_back(b);
  TF_ASSERT_OK_AND_ASSIGN(auto execute_outputs,
                          RunExecutable(inputs, {}, result, executable));

  TF_EXPECT_OK(PopulateCtxOutputsFromPjRtExecutableOutputs(
      /*num_missing_prefix_ctx_inputs=*/0, inputs, {}, *result,

    // Slice the first 'size' rows from the input tensorlist.
    auto guard = OpBuilder::InsertionGuard(*rewriter);
    auto inputs = branch_func.getFunctionType().getInputs();
    Block *block = rewriter->createBlock(
        &branch_func.getBody(), branch_func.begin(), inputs,
        SmallVector<Location>(inputs.size(), branch_func.getLoc()));

    Value scalar_zero = CreateI32SplatConst(loc, rewriter, {}, 0);

# RUN: tf_tfl_translate -tf-input-arrays=input -tf-input-shapes=1,1,1,256 -tf-input-data-types=DT_FLOAT -tf-inference-type=DT_QINT8  -tf-input-min-values='-33.614346' -tf-input-max-values='21.54917' -tf-output-arrays=output %s -o - --output-mlir 2>&1 | FileCheck --check-prefix=MLIR %s

node {
  name: "input"
  op: "Placeholder"
  attr {
    key: "dtype"
    value {
      type: DT_FLOAT
    }
  }
  attr {
    key: "shape"
    value {

# RUN: tf_tfl_translate -tf-input-arrays=input -tf-input-shapes=1,1,1,256 -tf-input-data-types=DT_FLOAT -tf-inference-type=DT_QINT8  -tf-input-min-values='-33.614346' -tf-input-max-values='21.54917' -tf-output-arrays=output %s -o - --output-mlir 2>&1 | FileCheck --check-prefix=MLIR %s

node {
  name: "input"
  op: "Placeholder"
  attr {
    key: "dtype"
    value {
      type: DT_FLOAT
    }
  }
  attr {
    key: "shape"
    value {