Full:     string(fullYaml),
		Redacted: string(redacted),
	}, nil
}

// writeKubeConfigFile will rewrite/replace the kubeconfig used by the CNI plugin.
// We are the only consumers of this file and it resides in our owned rundir on the host node,
// so we are good to simply write it out if our watched svcacct token changes.
func writeKubeConfigFile(cfg *config.InstallConfig) error {
	kc, err := createKubeConfig(cfg)

	for i, testCase := range testCases {
		// obtain the put bucket policy request body.
		// initialize HTTP NewRecorder, this records any mutations to response writer inside the handler.
		recV4 := httptest.NewRecorder()
		// construct HTTP request for PUT bucket policy endpoint.
		reqV4, err := newTestSignedRequestV4(http.MethodPut, getPutPolicyURL("", testCase.bucketName),

* Use the `templates` you created to render and return a `TemplateResponse`, pass the name of the template, the request object, and a "context" dictionary with key-value pairs to be used inside of the Jinja2 template.

```Python hl_lines="4  11  15-18"
{!../../docs_src/templates/tutorial001.py!}
```

/// note

Before FastAPI 0.108.0, Starlette 0.29.0, the `name` was the first parameter.

///

If you used to rely on this behavior, now you should create the resources for background tasks inside the background task itself, and use internally only data that doesn't depend on the resources of dependencies with `yield`.

Having another process manager inside the container (as would be with multiple workers) would only add **unnecessary complexity** that you are most probably already taking care of with your cluster system.

### Containers with Multiple Processes and Special Cases

Of course, there are **special cases** where you could want to have **a container** with several **Uvicorn worker processes** inside.

  dlm_tensor->dl_tensor.dtype = tf_dlm_type;

  std::vector<int64_t>* shape_arr = &tf_dlm_tensor_ctx->shape;
  std::vector<int64_t>* stride_arr = &tf_dlm_tensor_ctx->strides;
  shape_arr->resize(ndim);
  stride_arr->resize(ndim, 1);
  for (int i = 0; i < ndim; i++) {
    (*shape_arr)[i] = tensor->dim_size(i);
  }
  for (int i = ndim - 2; i >= 0; --i) {
    (*stride_arr)[i] = (*shape_arr)[i + 1] * (*stride_arr)[i + 1];

  outputs_.clear();
  for (TF_Operation* o : outputs) {
    outputs_.emplace_back(TF_Output{o, 0});
  }
  output_values_.resize(outputs_.size());
}

void CSession::SetOutputs(const std::vector<TF_Output>& outputs) {
  ResetOutputValues();
  outputs_ = outputs;
  output_values_.resize(outputs_.size());
}

void CSession::SetTargets(std::initializer_list<TF_Operation*> targets) {
  targets_.clear();

package json

import (
	"encoding/xml"
	"fmt"
	"strings"
)

const (
	document = "document"
	lines    = "lines"

	defaultRecordDelimiter = "\n"
)

// ReaderArgs - represents elements inside <InputSerialization><JSON/> in request XML.
type ReaderArgs struct {
	ContentType string `xml:"Type"`
	unmarshaled bool
}

// IsEmpty - returns whether reader args is empty or not.

}

// common - represents common elements inside <QueueConfiguration>, <CloudFunctionConfiguration>
// and <TopicConfiguration>
type common struct {
	ID     string `xml:"Id" json:"Id"`
	Filter S3Key  `xml:"Filter" json:"Filter"`
	Events []Name `xml:"Event" json:"Event"`
}

// Queue - represents elements inside <QueueConfiguration>
type Queue struct {
	common
	ARN ARN `xml:"Queue"`
}

      if (info.getName().endsWith("GwtSerializationDependencies")) {
        continue; // These classes exist only for the GWT compiler, not to be used.
      }
      if (
      /*
       * At least one of the classes nested inside TypeResolverTest triggers a bug under older JDKs:
       * https://bugs.openjdk.org/browse/JDK-8215328 -> https://bugs.openjdk.org/browse/JDK-8215470