<type>Notifier</type>
          </association>
        </field>
      </fields>
    </class>
    <class>
      <name>Notifier</name>
      <description>Configures one method for notifying users/developers when a build breaks.</description>
      <version>4.0.0+</version>
      <fields>
        <field>
          <name>type</name>
          <version>4.0.0+</version>

      return rewriter.notifyMatchFailure(
          conv_op,
          "Unsupported detphwise transpose convolution with non-1 channel "
          "multiplier");
    }

    // Slicing with dynamic offsets (helper method advised)
    auto create_slice = [&](mlir::Value tensor, int depth_idx, int channel_idx,
                            bool is_kernel = false) -> mlir::Value {
      std::vector<int64_t> tensor_shape =

namespace mlir {
namespace TF {
namespace {

// Compute a refined type between two types `lhs` and `rhs`, the result type
// is always more refined (i.e. has more static information) than `lhs`
// This method will actually merge the information contained in the
// types, it is capable of refining:
//   tensor<!tf_type.variant<tensor<?x8xf32>>>
// and:
//   tensor<!tf_type.variant<tensor<10x?xf32>>>
// into:

	response, err = s.client.Do(request)
	c.Assert(err, nil)
	verifyError(c, response, "SignatureDoesNotMatch", "The request signature we calculated does not match the signature you provided. Check your key and signing method.", http.StatusForbidden)
}

// This tests validate if PUT handler can successfully detect SHA256 mismatch.
func (s *TestSuiteCommon) TestSHA256Mismatch(c *check) {
	// generate a random bucket name.

}

//===----------------------------------------------------------------------===//
// TPUPartitionedInputV2
//===----------------------------------------------------------------------===//

// This method mimics this op's core/TF-level shape inference logic
LogicalResult TPUPartitionedInputV2Op::verify() {
  TPUPartitionedInputV2Op op = *this;

  int num_partitions = 1;

      },
      "io.k8s.api.flowcontrol.v1beta3.FlowDistinguisherMethod": {
        "description": "FlowDistinguisherMethod specifies the method of a flow distinguisher.",
        "properties": {
          "type": {
            "default": "",
            "description": "`type` is the type of flow distinguisher method The supported types are \"ByUser\" and \"ByNamespace\". Required.",
            "type": "string"
          }
        },

      },
      "io.k8s.api.flowcontrol.v1.FlowDistinguisherMethod": {
        "description": "FlowDistinguisherMethod specifies the method of a flow distinguisher.",
        "properties": {
          "type": {
            "default": "",
            "description": "`type` is the type of flow distinguisher method The supported types are \"ByUser\" and \"ByNamespace\". Required.",
            "type": "string"
          }
        },

        self.path_regex, self.path_format, self.param_convertors = compile_path(path)
        if methods is None:
            methods = ["GET"]
        self.methods: Set[str] = {method.upper() for method in methods}
        if isinstance(generate_unique_id_function, DefaultPlaceholder):
            current_generate_unique_id: Callable[
                ["APIRoute"], str

from tensorflow.python.saved_model import tag_constants
from tensorflow.python.training import checkpoint_utils
from tensorflow.python.types import core


# Type aliases for quantization method protobuf enums.
_PresetMethod = quant_opts_pb2.QuantizationMethod.PresetMethod
_CalibrationMethod = (
    stablehlo_quant_config_pb2.CalibrationOptions.CalibrationMethod
)

_QuantizationComponent = (

// If plugin is attachable, call WaitForAttach() and then mark the device
// as mounted. On next step, SetUpDevice is called without dependent of
// plugin type, but this method mainly is targeted for none attachable plugin.
// After setup is done, create symbolic links on both global map path and pod
// device map path. Once symbolic links are created, take fd lock by