Russ Cox <******@****.***> 1410149331 -0400

			t.Fatal(err)
		}
	}

	format := newFormatErasureV3(1, 8)
	format.Erasure.DistributionAlgo = formatErasureVersionV2DistributionAlgoV1
	formats := make([]*formatErasureV3, 8)

	for j := range 8 {
		newFormat := format.Clone()
		newFormat.Erasure.This = format.Erasure.Sets[0][j]
		formats[j] = newFormat
	}

	formats[1] = nil
	expThis := formats[2].Erasure.This
	formats[2].Erasure.This = ""

			osErrToFileErr(err)))
	}

	// format-V2 struct is exactly same as format-V1 except that version is "3"
	// which indicates the simplified multipart backend.
	formatV3 := formatErasureV3{}
	formatV3.Version = formatV2.Version
	formatV3.Format = formatV2.Format
	formatV3.Erasure = formatV2.Erasure
	formatV3.Erasure.Version = formatErasureVersionV3

	return json.Marshal(formatV3)
}

		formats: FormatUSTAR | FormatPAX | FormatGNU,
	}}

	for i, v := range vectors {
		formats, paxHdrs, err := v.header.allowedFormats()
		if formats != v.formats {
			t.Errorf("test %d, allowedFormats(): got %v, want %v", i, formats, v.formats)
		}
		if formats&FormatPAX > 0 && !maps.Equal(paxHdrs, v.paxHdrs) && !(len(paxHdrs) == 0 && len(v.paxHdrs) == 0) {

	FormatGNU

	// Schily's tar format, which is incompatible with USTAR.
	// This does not cover STAR extensions to the PAX format; these fall under
	// the PAX format.
	formatSTAR

	formatMax
)

func (f Format) has(f2 Format) bool   { return f&f2 != 0 }
func (f *Format) mayBe(f2 Format)     { *f |= f2 }
func (f *Format) mayOnlyBe(f2 Format) { *f &= f2 }
func (f *Format) mustNotBe(f2 Format) { *f &^= f2 }

<?xml version="1.0"?>
<assembly>
	<id>targz</id>
	<formats>
		<format>tar.gz</format>
	</formats>

	<includeBaseDirectory>false</includeBaseDirectory>

	<componentDescriptors>
		<componentDescriptor>src/main/assemblies/common-bin.xml
		</componentDescriptor>
	</componentDescriptors>

running the `tf.train.Server`.

## Untrusted inputs during training and prediction

TensorFlow supports a wide range of input data formats. For example it can
process images, audio, videos, and text. There are several modules specialized
in taking those formats, modifying them, and/or converting them to intermediate
formats that can be processed by TensorFlow.

These modifications and conversions are handled by a variety of libraries that

import (
	"errors"
	"net/http"
	"time"
)

// Supported amz date formats.
var amzDateFormats = []string{
	// Do not change this order, x-amz-date format is usually in
	// iso8601Format rest are meant for relaxed handling of other
	// odd SDKs that might be out there.
	"20060102T150405Z",
	time.RFC1123,
	time.RFC1123Z,
	// Add new AMZ date formats here.
}

// ErrMalformedDate always returned for dates that cannot be parsed.

<?xml version="1.0"?>
<assembly>
	<id>zip</id>
	<formats>
		<format>zip</format>
	</formats>

	<includeBaseDirectory>false</includeBaseDirectory>

	<componentDescriptors>
		<componentDescriptor>src/main/assemblies/common-bin.xml
		</componentDescriptor>
	</componentDescriptors>

            return StringUtil.EMPTY;
        }
        return date.format(DateTimeFormatter.ofPattern(Constants.ISO_DATETIME_FORMAT, Locale.ROOT));
    }

    /**
     * Formats a ZonedDateTime object using the specified format pattern.
     *
     * @param date the ZonedDateTime to format
     * @param format the date format pattern
     * @return formatted date string, or empty string if date is null
     */