cases := []struct {
		file            string
		recordDelimiter string
		fieldDelimiter  string
		header          bool
		wantColumns     []string
		wantTenFields   string
		totalFields     int
	}{
		{
			file:            "nyc-taxi-data-100k.csv",
			recordDelimiter: "\n",
			fieldDelimiter:  ",",
			header:          true,

</p>

<h3 id="String_types">String types</h3>

<p>
A <i>string type</i> represents the set of string values.
A string value is a (possibly empty) sequence of bytes.
The number of bytes is called the length of the string and is never negative.
Strings are immutable: once created,
it is impossible to change the contents of a string.
The predeclared string type is <code>string</code>;

In diesem Fall macht es Sinn, die Tags in einem `Enum` zu speichern.

**FastAPI** unterstützt diese genauso wie einfache Strings:

```Python hl_lines="1  8-10  13  18"
{!../../docs_src/path_operation_configuration/tutorial002b.py!}
```

## Zusammenfassung und Beschreibung

  TFE_OpReset(reused_op.get(), "Identity", custom_device_name, status.get());
  ASSERT_TRUE(TF_GetCode(status.get()) == TF_OK) << TF_Message(status.get());
  ASSERT_EQ(tensorflow::string(TFE_OpGetDevice(reused_op.get(), status.get())),
            tensorflow::string(custom_device_name));
  ASSERT_TRUE(TF_GetCode(status.get()) == TF_OK) << TF_Message(status.get());
  TFE_OpReset(reused_op.get(), "Identity",

    -----END CERTIFICATE-----
    """.trimIndent().decodeCertificatePem()

  private val client: OkHttpClient

  init {
    // This implementation just embeds the PEM files in Java strings; most applications will
    // instead read this from a resource file that gets bundled with the application.
    val certificates =
      HandshakeCertificates.Builder()

	case []byte:
		// TODO: only strings are supported in s3select output (not
		// binary arrays) - perhaps we need to check the annotation to
		// ensure it's UTF8 encoded.
		value = string(val)
	case [12]byte:
		// TODO: This is returned for the parquet INT96 type. We just
		// treat it same as []byte (but AWS S3 treats it as a large int)
		// - fix this later.
		value = string(val[:])
	case int32:
		value = int64(val)

pkg mime, func ExtensionsByType(string) ([]string, error)
pkg mime, method (*WordDecoder) Decode(string) (string, error)
pkg mime, method (*WordDecoder) DecodeHeader(string) (string, error)
pkg mime, method (WordEncoder) Encode(string, string) string
pkg mime, type WordDecoder struct
pkg mime, type WordDecoder struct, CharsetReader func(string, io.Reader) (io.Reader, error)
pkg mime, type WordEncoder uint8

```

////

## Set types

But then we think about it, and realize that tags shouldn't repeat, they would probably be unique strings.

And Python has a special data type for sets of unique items, the `set`.

Then we can declare `tags` as a set of strings:

//// tab | Python 3.10+

```Python hl_lines="12"
{!> ../../docs_src/body_nested_models/tutorial003_py310.py!}
```

////

					failed.UserPolicies = append(
						failed.UserPolicies,
						madmin.IAMErrPolicyEntity{
							Name:     u,
							Policies: strings.Split(pm.Policies, ","),
							Error:    err,
						})
				} else {
					added.UserPolicies = append(added.UserPolicies, map[string][]string{u: strings.Split(pm.Policies, ",")})
				}
			}
		}
	}

	// import group policy mappings
	{

  // Convert from TF_Output and TF_Tensor to a string and Tensor.
  std::vector<std::pair<string, Tensor>> input_pairs(ninputs);
  if (!TF_Run_Inputs(input_values, &input_pairs, status)) return;
  for (int i = 0; i < ninputs; ++i) {
    input_pairs[i].first = OutputName(inputs[i]);
  }

  // Convert from TF_Output to string names.
  std::vector<string> output_names(noutputs);
  for (int i = 0; i < noutputs; ++i) {