In the future, filters showing convergence in behavior across multiple
                                      implementations will be considered for inclusion in extended or core
                                      conformance levels. Filter-specific configuration for such filters

            "type": "string"
          },
          "filters": {
            "description": "Filters describes additional contraints that must be met when using the class.",
            "items": {
              "allOf": [
                {

                                  description: The destination_port value used by
                                    a filter chain's match condition.
                                  type: integer
                                filter:
                                  description: The name of a specific filter to apply
                                    the patch to.
                                  properties:

                                    a filter chain's match condition.
                                  maximum: 4294967295
                                  minimum: 0
                                  type: integer
                                filter:
                                  description: The name of a specific filter to apply
                                    the patch to.

                                    a filter chain's match condition.
                                  maximum: 4294967295
                                  minimum: 0
                                  type: integer
                                filter:
                                  description: The name of a specific filter to apply
                                    the patch to.

Computes a 2-D depthwise convolution given 4-D `input` and `filter` tensors.
  }];

  let description = [{
Given an input tensor of shape `[batch, in_height, in_width, in_channels]`
and a filter / kernel tensor of shape
`[filter_height, filter_width, in_channels, channel_multiplier]`, containing
`in_channels` convolutional filters of depth 1, `depthwise_conv2d` applies
a different filter to each input channel (expanding from 1 channel to

- Fixes an issue with the max-in-flight API server filter where the filter could take a long time to process an incoming request if it had been a long time since the last request. ([#96282](https://github.com/kubernetes/kubernetes/pull/96282), [@staebler](https://github.com/staebler)) [SIG API Machinery]

    *   Previously, TensorFlow error stack traces involved many internal frames,
        which could be challenging to read through, while not being actionable
        for end users. As of TF 2.7, TensorFlow filters internal frames in most
        errors that it raises, to keep stack traces short, readable, and focused
        on what's actionable for end users (their own code).

    This behavior can be disabled by calling

		{"(*UnaryExpr).End", Method, 0},
		{"(*UnaryExpr).Pos", Method, 0},
		{"(*ValueSpec).End", Method, 0},
		{"(*ValueSpec).Pos", Method, 0},
		{"(CommentMap).Comments", Method, 1},
		{"(CommentMap).Filter", Method, 1},
		{"(CommentMap).String", Method, 1},
		{"(CommentMap).Update", Method, 1},
		{"(ObjKind).String", Method, 0},
		{"ArrayType", Type, 0},
		{"ArrayType.Elt", Field, 0},

Class <org.gradle.api.internal.tasks.testing.filter.DefaultTestFilter> is not annotated (directly or via its package) with @org.gradle.api.NonNullApi in (DefaultTestFilter.java:0)
Class <org.gradle.api.internal.tasks.testing.filter.TestFilterSpec> is not annotated (directly or via its package) with @org.gradle.api.NonNullApi in (TestFilterSpec.java:0)