func.func private @whitespace_tokenizer_rank1(%arg0: tensor<1x!tf_type.string> {tf._user_specified_name = "input"}) -> (tensor<?x!tf_type.string>, tensor<?xi64>) attributes {tf._input_shapes = [#tf_type.shape<1>], tf._implements = #tf_type.func<@"tftext:WhitespaceTokenizer", {}>, tf.signature.is_stateful} {
  %0 = "tf.Const"() {value = dense<[0, 1]> : tensor<2xi64>} : () -> tensor<2xi64>

// InferShapeForFunction.
FailureOr<bool> InferModuleShape(ModuleOp module, int64_t max_iterations = 10,
                                 ArrayRef<TypeID> ops_to_skip = {},
                                 ArrayRef<ArrayRef<int64_t>> input_shapes = {});

// Given a tensorflow NodeShape string, returns a vector of argument shapes
// that can be used with InferShapeForFunction.
// TF NodeShape uses `,` to separate dimensions, and `:` to separate arguments.

// Converts an TensorFlow shape to the one used in MLIR.
void ConvertToMlirShape(const TensorShape& input_shape,
                        llvm::SmallVectorImpl<int64_t>* shape);

// Converts an TensorFlow shape proto to the one used in MLIR.
Status ConvertToMlirShape(const TensorShapeProto& input_shape,
                          llvm::SmallVectorImpl<int64_t>* shape);

                               {{"dtype", DT_FLOAT}, {"shape", input_shape}});
  *graph.add_node() = MakeNode("b", "Placeholder", {},
                               {{"dtype", DT_FLOAT}, {"shape", input_shape}});
  *graph.add_node() = MakeNode("c", "Placeholder", {},
                               {{"dtype", DT_FLOAT}, {"shape", input_shape}});
  *graph.add_node() = MakeNode("m", "TestFn", {"a", "b", "c"}, {});
  return graph;
}

// CHECK-LABEL: func private @batched_function
// CHECK: arg1
func.func private @batched_function(%arg0: tensor<1x3xf32>, %arg1: tensor<*x!tf_type.resource>) -> tensor<1x3xf32>
  attributes {tf._input_shapes = [#tf_type.shape<1x3>, #tf_type.shape<*>], tf.signature.is_stateful} {
  // CHECK: [[handle:%.*]] = "tf.VarHandleOp"()
  // CHECK: "tf.ReadVariableOp"([[handle]])

  TensorShape updates_shape({4});
  TensorShape input_shape({8});
  auto input = Placeholder(scope_, DT_FLOAT, Placeholder::Shape(input_shape));
  auto updates =
      Placeholder(scope_, DT_FLOAT, Placeholder::Shape(updates_shape));
  auto indices = Const(scope_, {{4}, {3}, {1}, {7}});
  auto y = ScatterNdNonAliasingAdd(scope_, input, indices, updates);
  RunTest({input, updates}, {input_shape, updates_shape}, {y}, {input_shape});
}

                                     Value &padding, int num_dims) {
  ShapedType input_shape = mlir::cast<ShapedType>(input.getType());
  SmallVector<int64_t> spatial_dims(num_dims - 2);
  absl::c_iota(spatial_dims, 1);
  bool has_dynamic_spatial_dim = absl::c_any_of(
      spatial_dims,
      [&input_shape](int64_t dim) { return input_shape.isDynamicDim(dim); });
  if (conv_padding.strref() == "SAME" && has_dynamic_spatial_dim) {

      target_opset: quant_opts_pb2.OpSet,
  ):
    lhs_batch_size, rhs_batch_size = batch_sizes
    input_shape = (*lhs_batch_size, 1, 1024)
    filter_shape = (*rhs_batch_size, 1024, 3)
    static_input_shape = [dim if dim is not None else 2 for dim in input_shape]
    model = self._create_matmul_model(
        input_shape,
        filter_shape,
        self._input_saved_model_path,
        has_bias,

                                    StringAttr conv_padding,
                                    ArrayAttr explicit_paddings) {
  auto input_shape = mlir::cast<ShapedType>(input.getType());
  auto filter_shape = mlir::cast<ShapedType>(filter.getType());
  if (!input_shape.hasRank() || input_shape.getRank() != 4 ||
      !filter_shape.hasRank() || filter_shape.getRank() != 4) {
    emitError(loc, "input and filter are expected to be 4D tensors");

  Shape::Attrs shape_attrs;
  shape_attrs.out_type_ = op.input_type(1);
  auto input_shape = Shape(scope, op.input(0), shape_attrs);
  // We interleave multiples and input_shape to get split_shape,
  // reshape grad to split_shape, and reduce along all even
  // dimensions (the tiled dimensions) to get the result
  // with shape input_shape.  For example
  //   input_shape = [20, 30, 40]
  //   multiples = [2, 3, 4]