namespace internal {

void CompareNumericalAndAutodiffGradients(
    Model model, Model grad_model, AbstractContext* ctx,
    absl::Span<AbstractTensorHandle* const> inputs, bool use_function,
    double abs_error) {
  auto num_inputs = inputs.size();
  std::vector<AbstractTensorHandle*> outputs(num_inputs);
  auto s = RunModel(grad_model, ctx, inputs, absl::MakeSpan(outputs),
                    /*use_function=*/use_function);

#include "tensorflow/c/eager/unified_api_testutil.h"

namespace tensorflow {
namespace gradients {
namespace internal {

void CompareNumericalAndAutodiffGradients(
    Model model, Model grad_model, AbstractContext* ctx,
    absl::Span<AbstractTensorHandle* const> inputs, bool use_function,
    double abs_error = 1e-2);

void CheckTensorValue(AbstractTensorHandle* t, absl::Span<const float> manuals,

					this.get_container()
						.bind("load_node.jstree", $.proxy(function (e, data) {
								var o = this._get_node(data.rslt.obj),
									t = this;
								if(o === -1) { o = this.get_container_ul(); }
								if(!o.length) { return; }
								o.find("li.jstree-open:not(:has(ul))").each(function () {
									t.load_node(this, $.noop, $.noop);
								});
							}, this));
				}

### Breaking Changes


`tf.keras`:

	<component name="languageHelper" class="org.codelibs.fess.helper.LanguageHelper">
		<property name="detector">
			<component class="org.apache.tika.langdetect.optimaize.OptimaizeLangDetector">
				<postConstruct name="loadModels"></postConstruct>
			</component>
		</property>
	</component>
	<component name="searchLogHelper" class="org.codelibs.fess.helper.SearchLogHelper">
		<!-- 
		<property name="userCheckInterval">5 * 60 * 1000</property>

            action()
        }
    }

    override fun <T : Any> loadOrCreateModel(creator: () -> T): T {
        if (isLoaded) {
            runLoadedSideEffects()
            return loadModel().uncheckedCast()
        }

        return runWorkThatContributesToCacheEntry {
            val model = creator()
            saveModel(model)
            model
        }
    }

    private

@app.get("/dict", response_model=Model, response_model_by_alias=False)
def read_dict():
    return {"alias": "Foo"}


@app.get("/model", response_model=Model, response_model_by_alias=False)
def read_model():
    return Model(alias="Foo")


@app.get("/list", response_model=List[Model], response_model_by_alias=False)
def read_list():
    return [{"alias": "Foo"}, {"alias": "Bar"}]

    std::vector<int>& used_tensors, bool quantized = false) {
  std::unique_ptr<ModelT> quant_model =
      CreateMutableModelFromFile(quantized_model);
  std::unique_ptr<ModelT> float_model =
      CreateMutableModelFromFile(expected_model);

  for (int i = 0; i < possible_tensors->size(); i++) {
    // Skip if the tensor is already used for match.