auto input_args_have_static_shape = [&]() -> bool {
    return mlir::cast<TensorType>(input_tensor.getType()).hasStaticShape() &&
           mlir::cast<TensorType>(kernel_tensor.getType()).hasStaticShape() &&
           mlir::cast<TensorType>(window_strides.getType()).hasStaticShape() &&
           mlir::cast<TensorType>(padding.getType()).hasStaticShape() &&
           mlir::cast<TensorType>(lhs_dilation.getType()).hasStaticShape() &&

  }

  // Get the tensor types.
  const auto input_type = input_tensor.getType().cast<ShapedType>();
  const auto weights_type = weights_tensor.getType().cast<ShapedType>();
  const auto bias_type =
      has_bias ? bias_tensor.getType().cast<ShapedType>() : ShapedType{};

  const auto output_type = getType(0).cast<ShapedType>();

  // Folding only implemented for float tensors.

    Operation* filter_op = filter_value.getDefiningOp();
    auto filter_uniform_quantized_type =
        GetElementType(filter_value).cast<UniformQuantizedPerAxisType>();
    auto filter_constant_value_attr = cast<DenseIntElementsAttr>(
        cast<stablehlo::ConstantOp>(filter_value.getDefiningOp()).getValue());
    const DenseIntElementsAttr new_filter_value_attr =

  }
}

LogicalResult SpaceToBatchNDOp::verify() {
  SpaceToBatchNDOp op = *this;
  const auto input_type = op.getInput().getType().cast<TensorType>();
  const auto block_shape_type = op.getBlockShape().getType().cast<TensorType>();
  const auto paddings_type = op.getPaddings().getType().cast<TensorType>();

  // Check that block_shape has rank 1.
  if (!IsOfRankOrUnranked(op.getBlockShape(), 1)) {

  // types match the result types.
  auto yield = cast<YieldOp>(region.front().getTerminator());
  auto updated_results = llvm::to_vector<4>(yield.getOperands());

  // If the yield types do not match the IfRegion result types, add appropriate
  // casts.
  rewriter.setInsertionPoint(yield);
  for (auto it : llvm::zip(op.getResultTypes(), updated_results)) {

// 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)
// after the TF -> TFL legalization pass is done.
// LINT.IfChange
def TFL_CastOp : TFL_Op<"cast", [
    Pure,
    SameOperandsAndResultShape]> {
  let summary = "Cast operator";

  let description = [{
    Casts input from input type to output type.
  }];

TypeSafeDiagnosingMa<T> extends BaseMatcher<T> Convenient base class for Matchers that require a non-null value of a specific type and that will report why the received value has been rejected. This implements the null check, checks the type and then casts. To use, implement matchesSafely() . Author: Neil Dunn, Nat Pryce, Steve Freeman Constructor Summary protected TypeSafeDiagnosingMa() The default constructor for simple sub types protected TypeSafeDiagnosingMa(java.lang.Class<?> expectedType) Use this...

bool IsLastDimEqualToNumElements(Type type1, Type type2) {
  return (mlir::cast<ShapedType>(type1).getRank() >= 1 &&
          mlir::cast<ShapedType>(type1).getDimSize(
              mlir::cast<ShapedType>(type1).getRank() - 1) ==
              mlir::cast<ShapedType>(type2).getNumElements());
}

bool CanFuseConvOrDepthwiseConvShapes(const ArrayRef<int64_t> filter_shape,

func TestBuildGatewayClustersWithRingHashLb(t *testing.T) {
	cases := []struct {
		name             string
		ringSize         int
		expectedRingSize int
	}{
		{
			"default",
			0,
			1024,
		},
		{
			"ring size",
			2,
			2,
		},
		{
			"use ring hash",
			2,
			2,
		},
	}
	for _, tt := range cases {
		t.Run(tt.name, func(t *testing.T) {
			g := NewWithT(t)

    // CHECK-SAME: (tensor<*xf32>) -> tensor<*xi8>
    // CHECK: %[[TENSOR_CAST:.*]] = tensor.cast %[[CAST_RESULT_2]] : tensor<*xi8> to tensor<?x?x?x?xi8>
    %2 = tensor.cast %28 : tensor<*xi8> to tensor<?x?x?x?xi8>
    // CHECK: %[[CAST_RESULT_0:.*]] = "tf.Cast"(%arg0)
    // CHECK-SAME: (tensor<16x194x199x4xf32>) -> tensor<16x194x199x4xui8>
    // CHECK: %[[CAST_RESULT_1:.*]] = "tf.Cast"(%arg0)