for arithmetic operations. Two shapes are compatible if for each
dimension pair they are either equal or one of them is one. When trying
to broadcast a Tensor to a shape, it starts with the trailing dimensions,
and works its way forward.

For example,

>>> x = tf.constant([1, 2, 3])
>>> y = tf.broadcast_to(x, [3, 3])
>>> print(y)
tf.Tensor(
    [[1 2 3]
     [1 2 3]

  IFS=' ' read -ra FLAGS <<< "$1"
  params=""
  for flag in "${FLAGS[@]}"; do
    params+="\n\"$flag\","
  done
  if [ -n "$params" ]; then
    echo "${params::-1}"  #  drop trailing comma
  fi
}

function append-param-if-not-present {
  # A helper function to add flag to an arguments string
  # if no such flag is present already
  local params="$1"
  local -r flag="$2"

			break
		}
		prefix = append(prefix, byte('\t'))
	}
	// Remove prefix from all tabs, fail otherwise.
	for i := range lines {
		line := lines[i]
		// It's OK for the last line to be blank (trailing \n)
		if i == len(lines)-1 && len(line) <= len(prefix) && bytes.TrimSpace(line) == nil {
			lines[i] = []byte{}
			break
		}
		if !bytes.HasPrefix(line, prefix) {
			minRange := i - 5

			i = 0
		} else {
			i = bytes.Index(data, modulePrefix)
			if i < 0 {
				return ""
			}
			i++
		}
		line := data[i:]

		// Cut line at \n, drop trailing \r if present.
		if j := bytes.IndexByte(line, '\n'); j >= 0 {
			line = line[:j]
		}
		if line[len(line)-1] == '\r' {
			line = line[:len(line)-1]
		}
		line = line[len("module "):]

                @app.get("/items/")
                async def read_items():
                    return [{"item_id": "Foo"}]
                ```

                With this app, if a client goes to `/items` (without a trailing slash),
                they will be automatically redirected with an HTTP status code of 307
                to `/items/`.
                """
            ),
        ] = True,
        docs_url: Annotated[

func (ab *AsmBuf) Put(b []byte) {
	copy(ab.buf[ab.off:], b)
	ab.off += len(b)
}

// PutOpBytesLit writes zero terminated sequence of bytes from op,
// starting at specified offset (e.g. z counter value).
// Trailing 0 is not written.
//
// Intended to be used for literal Z cases.
// Literal Z cases usually have "Zlit" in their name (Zlit, Zlitr_m, Zlitm_r).
func (ab *AsmBuf) PutOpBytesLit(offset int, op *opBytes) {

	op_VCHL   uint32 = 0xE7F9 // 	VRR-b	VECTOR COMPARE HIGH LOGICAL
	op_VCLZ   uint32 = 0xE753 // 	VRR-a	VECTOR COUNT LEADING ZEROS
	op_VCTZ   uint32 = 0xE752 // 	VRR-a	VECTOR COUNT TRAILING ZEROS
	op_VEC    uint32 = 0xE7DB // 	VRR-a	VECTOR ELEMENT COMPARE
	op_VECL   uint32 = 0xE7D9 // 	VRR-a	VECTOR ELEMENT COMPARE LOGICAL
	op_VERIM  uint32 = 0xE772 // 	VRI-d	VECTOR ELEMENT ROTATE AND INSERT UNDER MASK

		return true
	}
	return sequenceOfOnes(x) || sequenceOfOnes(^x)
}

// sequenceOfOnes tests whether a constant is a sequence of ones in binary, with leading and trailing zeros.
func sequenceOfOnes(x uint64) bool {
	y := x & -x // lowest set bit of x. x is good iff x+y is a power of 2
	y += x
	return (y-1)&y == 0
}

          </association>
        </field>
      </fields>
      <comment>We could probably have a specific element for a dev mailing list for things like CI,
        and maybe even a specific element for the user and scm mailing lists. Then leave the more
        lose structure for any other type of mailing list.</comment>
      <codeSegments>
        <codeSegment>
          <version>4.0.0+</version>
          <code>

from tensorflow.python.saved_model import signature_constants
from tensorflow.python.saved_model import signature_def_utils_impl
from tensorflow.python.saved_model import tag_constants
from tensorflow.python.training import checkpoint_utils
from tensorflow.python.types import core


# Type aliases for quantization method protobuf enums.
_PresetMethod = quant_opts_pb2.QuantizationMethod.PresetMethod
_CalibrationMethod = (