inferred type arguments.
Otherwise, type inference fails and the program is invalid.
</p>

<p>
Type inference uses the type relationships between pairs of types for inference:
For instance, a function argument must be <a href="#Assignability">assignable</a>
to its respective function parameter; this establishes a relationship between the
type of the argument and the type of the parameter.

              getLowerEndpoint(),
              getLowerBoundType());
      result.reverse = this;
      return this.reverse = result;
    }
    return result;
  }

  // This method helps J2KT's type inference.
  private static <T extends @Nullable Object> Comparator<T> reverseComparator(
      Comparator<T> comparator) {
    return Ordering.from(comparator).reverse();
  }

  @Override
  public String toString() {

      status->status = TF_TensorToTensor(input_tensors[i], &input_tensor);
      if (!status->status.ok()) return;
      input_tensors_vector[i] = &input_tensor;
    }
  }

  // Create an inference context with dummy values, which will be updated later.
  InferenceContext c(TF_GRAPH_DEF_VERSION, node_def, op_reg_data->op_def,
                     std::vector<ShapeHandle>(num_inputs), input_tensors_vector,

      SortedMap<K, ? extends V> sortedLeft = (SortedMap<K, ? extends V>) left;
      return difference(sortedLeft, right);
    }
    return difference(left, right, Equivalence.equals());
  }

  /**
   * Computes the difference between two maps. This difference is an immutable snapshot of the state
   * of the maps at the time this method is called. It will never change, even if the maps change at
   * a later time.
   *

      SortedMap<K, ? extends V> sortedLeft = (SortedMap<K, ? extends V>) left;
      return difference(sortedLeft, right);
    }
    return difference(left, right, Equivalence.equals());
  }

  /**
   * Computes the difference between two maps. This difference is an immutable snapshot of the state
   * of the maps at the time this method is called. It will never change, even if the maps change at
   * a later time.
   *

*   Fixes an undefined behavior arising from reference binding to nullptr in
    shape inference
    ([CVE-2021-37676](https://cve.mitre.org/cgi-bin/cvename.cgi?name=CVE-2021-37676))
*   Fixes a division by 0 in most convolution operators
    ([CVE-2021-37675](https://cve.mitre.org/cgi-bin/cvename.cgi?name=CVE-2021-37675))
*   Fixes vulnerabilities arising from missing validation in shape inference for
    `Dequantize`

    }
    status->status = desc->node_builder.Finalize(&desc->graph->graph, &ret,
                                                 /*consume=*/true);

    if (status->status.ok()) {
      // Run shape inference function for newly added node.
      status->status = desc->graph->refiner.AddNode(ret);
    }
    if (status->status.ok()) {
      // Add the node to the name-to-node mapping.

//   example, one cannot use the output of "switch" node as input.
// - `inputs` and `outputs` cannot have reference types. Reference types are
//   not exposed through C API and are being replaced with Resources. We support
//   reference types inside function's body to support legacy code. Do not
//   use them in new code.
// - Every node in the function's body must have all of its inputs (including

  }

  @SuppressWarnings("JUnitIncompatibleType")
  public void testWhere() {
    assertEquals(new TypeToken<Map<String, Integer>>() {}, mapOf(String.class, Integer.class));
    // Type inference is doomed here: int.class is the same as Integer.class, so this is comparing
    // TypeToken<int[]> and TypeToken<Integer[]>.
    assertEquals(new TypeToken<int[]>() {}, arrayOf(int.class));

- Introduced the Node Declared Features capability (alpha), which includes:
  - A new `Node.Status.DeclaredFeatures` field for publishing node-specific features.
  - A `component-helpers` library for feature registration and inference.
  - A `NodeDeclaredFeatures` scheduler plugin to match pods with nodes that provide required features.