y_shape = [v if v is not None else n for v in shapes[1]]

    class MatmulModel(module.Module):

      def __init__(self, bias: Optional[core.Tensor]):
        self._bias = bias
        self._kernel = np.random.uniform(size=y_shape).astype('f4')
        self._min = (-0.8, -0.8, -0.9)
        self._max = (0.9, 0.9, 1.0)

      @def_function.function(
          input_signature=[

                              op->getResult(0));
  return op->getResult(0).getType().cast<TensorType>().clone(
      new_result_quantized_type);
}

// Matches kernel dimension numbers, ranks of input and output and constant
// kernel for legalization to TFLite convolution ops.
LogicalResult MatchConvolutionFormat(stablehlo::ConvolutionOp op) {
  stablehlo::ConvDimensionNumbersAttr dimension_numbers =

    // Mirror the filter in the spatial dimensions.
    mlir::Value reverse_filter_in = conv_op.getRhs();
    // If the kernel is with format anythoing other than HWOI, we
    // transpose it to [0,1,o,i] as the TF->TFL pass anticipates this and the
    // kernel format information will be lost once we legalize to TF
    if (!isKernelFormatHWOI(dnums)) {
      SmallVector<int64_t, 4> permutation;

          rewriter.getUnitAttr());
      op.getBiasMutable().assign(none_value);
      return success();
    }

    return failure();
  }
};

// Return true if the given Add operation has the CPU kernel supported shapes.
bool VerifyAddOpShapeConstraints(AddOp op) {
  auto element_type = getElementTypeOrSelf(op.getOutput().getType());

  // Allows F32, QI8, QUI8 and I32 outputs when the operands have valid shapes,

	// bound to.
	DF_1_NODIRECT DynFlag1 = 0x00020000
	// Reserved for internal use by the kernel runtime-linker.
	DF_1_IGNMULDEF DynFlag1 = 0x00040000
	// Reserved for internal use by the kernel runtime-linker.
	DF_1_NOKSYMS DynFlag1 = 0x00080000
	// Reserved for internal use by the kernel runtime-linker.
	DF_1_NOHDR DynFlag1 = 0x00100000

  gci-beta-55-8872-47-0:
  Date:           Nov 11, 2016
  Kernel:         ChromiumOS-4.4
  Kubernetes:     v1.4.5
  Docker:         v1.11.2
  Changelog (vs 55-8872-18-0)
    * Cherry-pick runc PR#608: Eliminate redundant parsing of mountinfo
    * Updated kubernetes to v1.4.5

  # /sbin/crash_reporter which is more restrictive in saving crash dumps. So for
  # now, set a generic core_pattern that users can work with.
  echo "/core.%e.%p.%t" > /proc/sys/kernel/core_pattern
}

# secure_random generates a secure random string of bytes. This function accepts
# a number of secure bytes desired and returns a base64 encoded string with at

    return op.emitOpError()
           << "kernel tensor argument is " << kernel_tensor_shape.size()
           << " which is invalid, since kernel tensor argument must has a "
           << "rank greater than 2.\n";
  }

  if (input_tensor_shape.size() != kernel_tensor_shape.size()) {
    return op.emitOpError() << "both input tensor and kernel tensor must "

  );

  // TFLite kernel only supports up to 5D input including added axis.
  let hasVerifier = 1;

  let hasOptions = 1;

  let hasFolder = 1;
}

// If there is a change in supporting more types in the TFLite cast op kernel,
// the While loop outline pass should be updated since it inserts cast op(s)

			return 0, 0, fmt.Errorf("failed to get kernel version, unable to determine if feature %s can be supported : %w",
				features.UserNamespacesSupport, err)
		}
		if kernelVersion != nil && !kernelVersion.AtLeast(version.MustParseGeneric(utilkernel.UserNamespacesSupportKernelVersion)) {
			klog.InfoS("WARNING: the kernel version is incompatible with the feature gate, which needs as a minimum kernel version",