* then we call it in a separate thread (using pseudoTimedGet). The result is that we wait as long
   * as necessary when the method is expected to return (at the cost of hanging forever if there is
   * a bug in the class under test) but that we time out fairly promptly when the method is expected
   * to hang (possibly too quickly, but too-quick failures should be very unlikely, given that we

   * then we call it in a separate thread (using pseudoTimedGet). The result is that we wait as long
   * as necessary when the method is expected to return (at the cost of hanging forever if there is
   * a bug in the class under test) but that we time out fairly promptly when the method is expected
   * to hang (possibly too quickly, but too-quick failures should be very unlikely, given that we

    return ConstOp::build(builder, result, elem_attr);
  } else if (mlir::isa<BoolAttr, FloatAttr, IntegerAttr>(value)) {
    // All TensorFlow types must be tensor types. In the build() method,
    // we want to provide more flexibility by allowing attributes of scalar
    // types. But we need to wrap it up with ElementsAttr to construct
    // valid TensorFlow constants.

                 llvm::ArrayRef<mlir::NamedAttribute> attrs);

  // Finds out the function definition for the given function name from the
  // graph and converts it to a function of the module. This method is called
  // on demand because the graph flib_def does not provide an iterator
  // interface.
  Status ConvertLibFunction(llvm::StringRef func_name);

		name:    name,
		size:    int64(len(data)),
		mode:    fileMode,
		modTime: UTCNow(),
		isDir:   false,
		sys:     nil,
	})
	if zerr != nil {
		return zerr
	}
	header.Method = zip.Deflate
	zwriter, zerr := zipWriter.CreateHeader(header)
	if zerr != nil {
		return zerr
	}
	_, err := io.Copy(zwriter, bytes.NewReader(data))
	return err
}

            <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;