"options": {
        "alertThreshold": true
      },
      "percentage": false,
      "pluginVersion": "10.0.2",
      "pointradius": 2,
      "points": false,
      "renderer": "flot",
      "seriesOverrides": [],
      "spaceLength": 10,
      "stack": false,
      "steppedLine": false,
      "targets": [
        {
          "datasource": {
            "type": "prometheus",

repositories {
    maven {
        name = "Gradle public repository"
        url = uri("https://repo.gradle.org/gradle/public")
        content {
            includeGroup("net.rubygrapefruit")
            includeModule("flot", "flot")
            includeModule("org.gradle", "gradle-tooling-api")
            includeModule("org.gradle.buildtool.internal", "configuration-cache-report")

      },
      "lines": true,
      "linewidth": 1,
      "links": [],
      "nullPointMode": "null",
      "percentage": false,
      "pointradius": 5,
      "points": false,
      "renderer": "flot",
      "seriesOverrides": [],
      "spaceLength": 10,
      "stack": false,
      "steppedLine": false,
      "targets": [
        {
          "datasource": {
            "type": "prometheus",

TEST(CAPI, TestFromProto) {
  Tensor t_cc(DT_FLOAT, TensorShape({2, 3}));
  t_cc.flat<float>().setConstant(1.0);
  tensorflow::TensorProto t_proto;
  t_cc.AsProtoField(&t_proto);

  TF_Buffer* t_buffer = TF_NewBuffer();
  TF_CHECK_OK(MessageToBuffer(t_proto, t_buffer));

  const int num_bytes = 6 * sizeof(float);
  float* values =
      reinterpret_cast<float*>(tensorflow::cpu_allocator()->AllocateRaw(

# Python Types Intro

Python has support for optional "type hints" (also called "type annotations").

These **"type hints"** or annotations are a special syntax that allow declaring the <abbr title="for example: str, int, float, bool">type</abbr> of a variable.

By declaring types for your variables, editors and tools can give you better support.

                },
                new float[] {(float) 2, (float) 3, (float) 4}))
        .isEqualTo(1);
    assertThat(
            Floats.indexOf(
                new float[] {(float) 4, (float) 3, (float) 2},
                new float[] {(float) 2, (float) 3, (float) 4}))
        .isEqualTo(-1);

    for (float value : NUMBERS) {

  auto ReluGradModel = BuildGradModel(ReluModel, registry_);

  float X_vals[] = {1.0f, 2.0f, 3.0f, -5.0f, -4.0f, -3.0f, 2.0f, 10.0f, -1.0f};
  int64_t X_dims[] = {3, 3};
  AbstractTensorHandlePtr X;
  {
    AbstractTensorHandle* X_raw;
    status_ = TestTensorHandleWithDims<float, TF_FLOAT>(
        immediate_execution_ctx_.get(), X_vals, X_dims, 2, &X_raw);

        flat_dependant.security_requirements.extend(flat_sub.security_requirements)
    return flat_dependant


def get_flat_params(dependant: Dependant) -> List[ModelField]:
    flat_dependant = get_flat_dependant(dependant, skip_repeats=True)
    return (
        flat_dependant.path_params
        + flat_dependant.query_params
        + flat_dependant.header_params
        + flat_dependant.cookie_params

   * @return a hash code for the value
   */
  public static int hashCode(float value) {
    // TODO(kevinb): is there a better way, that's still gwt-safe?
    return ((Float) value).hashCode();
  }

  /**
   * Compares the two specified {@code float} values using {@link Float#compare(float, float)}. You
   * may prefer to invoke that method directly; this method exists only for consistency with the

def get_path_param_gt(item_id: float = Path(gt=3)):
    return item_id


@app.get("/path/param-gt0/{item_id}")
def get_path_param_gt0(item_id: float = Path(gt=0)):
    return item_id


@app.get("/path/param-ge/{item_id}")
def get_path_param_ge(item_id: float = Path(ge=3)):
    return item_id


@app.get("/path/param-lt/{item_id}")
def get_path_param_lt(item_id: float = Path(lt=3)):
    return item_id