Arg<TensorOf<[TF_Int16, TF_Int32, TF_Int64]>, [{int16, int32 or int64, must be in the range `[-rank(input), rank(input))`.
Describes which dimension of the input Tensor to reduce across. For vectors,
use dimension = 0.}]>:$dimension
  );

  let results = (outs

    requests with the actual input and output keys used by the trainer code, or
    conversely, update the trainer code to name the input and output Tensors
    'inputs' and 'outputs', respectively. Signatures using the 'classify' and
    'regress' methods are not affected by this change; they will continue to
    standardize their input and output keys as before.
*   Add in-memory caching to the Dataset API.

                                        before the action. It appends to any existing values associated
                                        with the header name.


                                        Input:
                                          GET /foo HTTP/1.1
                                          my-header: foo


                                        Config:

	}
	for k, item := range table {
		err := validateResourceName(item.input, field.NewPath("field"))
		if len(err) != 0 && item.success {
			t.Errorf("expected no failure for input %q", item.input)
		} else if len(err) == 0 && !item.success {
			t.Errorf("expected failure for input %q", item.input)
			for i := range err {
				detail := err[i].Detail

        "description": "Arguments to the entrypoint. The container image's CMD is used if this is not provided. Variable references $(VAR_NAME) are expanded using the container's environment. If a variable cannot be resolved, the reference in the input string will be unchanged. Double $$ are reduced to a single $, which allows for escaping the $(VAR_NAME) syntax: i.e. \"$$(VAR_NAME)\" will produce the string literal \"$(VAR_NAME)\". Escaped references will never be expanded, regardless of whether...