"≡":                       "\u2261",
	"∯":                          "\u222f",
	"∮":                 "\u222e",
	"ℂ":                            "\u2102",
	"∐":                       "\u2210",
	"&CounterClockwiseContourIntegral;": "\u2233",
	"⨯":                           "\u2a2f",
	"𝒞":                            "\U0001d49e",

ARISING IN ANY WAY OUT OF THE USE OF THIS * SOFTWARE, EVEN IF ADVISED OF THE POSSIBILITY OF SUCH DAMAGE. ********** META-INF/NOTES.TXT This product includes software developed by the Indiana University Extreme! Lab (http://www.extreme.indiana.edu/). This product includes software developed by The Apache Software Foundation (http://www.apache.org/). This product includes software developed by ThoughtWorks (http://www.thoughtworks.com). org/codehaus/plexus/util/Base64.class package org.codehaus.plexus.util;...

	// Special optimization, for very short buffers
	CMPQ inl, $192
	JBE  openAVX2192
	CMPQ inl, $320
	JBE  openAVX2320

	// For the general key prepare the key first - as a byproduct we have 64 bytes of cipher stream
	VMOVDQA BB0, state1StoreAVX2
	VMOVDQA CC0, state2StoreAVX2
	VMOVDQA DD0, ctr3StoreAVX2
	MOVQ    $10, itr2

openAVX2PreparePolyKey:
	chachaQR_AVX2(AA0, BB0, CC0, DD0, TT0)

  // Do the actual folding, one output at a time.
  std::vector<float> result_values;
  result_values.reserve(output_size);

  for (int i = 0; i < output_size; ++i) {
    // Dot product with Kahan/Neumaier summation to minimize numeric errors.
    float sum = has_bias ? *bias_values_it : 0.0f;
    float compensation = 0.0f;
    for (int j = 0; j < input_size; ++j) {

      TFL_TCresVTEtIsSameAsOp<0, 0>>,
    QuantizableResult,
    Pure]> {
  let summary = "Prod-reduction operator";

  let description = [{
    Computes the product along the specified axes
  }];

  let arguments = (ins
    TFL_TensorOf<[F32, I32, I64, QI8, QUI8, TFL_Quint8, QI16]>:$input,
    TFL_I32Tensor:$axes,
    BoolAttr:$keep_dims
  );

    this->enable_canonicalization_ = enable_canonicalization;
    this->disable_fuse_mul_and_fc_ = disable_fuse_mul_and_fc;
  }

  void runOnOperation() override;
};

// Return true if the product of dimension values of a subsection of the tensor
// is equal to the non-contracting dimension after a reshape
bool BroadcastDimsProductEqual(Value input, Value output,
                               size_t agg_start_idx) {

				})
			}
		}
	}
}

// autoPassthroughCases tests that we cannot hit unexpected destinations when using AUTO_PASSTHROUGH
func autoPassthroughCases(t TrafficContext) {
	// We test the cross product of all Istio ALPNs (or no ALPN), all mTLS modes, and various backends
	alpns := []string{"istio", "istio-peer-exchange", "istio-http/1.0", "istio-http/1.1", "istio-h2", ""}
	modes := []string{"STRICT", "PERMISSIVE", "DISABLE"}

    }
    return success();
  }

  // Detect if reshape output shape is folded.
  bool shape_ty_zero_dim = false;
  int unknown_index = -1;
  // The product of constant shape argument excluding unknown dimension.
  int64_t shape_ty_size = 1;
  llvm::SmallVector<int64_t, 8> output_ty_shape;
  output_ty_shape.reserve(shape_attr.getNumElements());

				}},
				Match: []*networking.HTTPMatchRequest{
					{
						Uri: &networking.StringMatch{
							MatchType: &networking.StringMatch_Prefix{Prefix: "/api/v1/product"},
						},
					},
					{
						Uri: &networking.StringMatch{
							MatchType: &networking.StringMatch_Prefix{Prefix: "/api/v1/products"},
						},
						Method: &networking.StringMatch{

# quantization is enabled. Respectively refers to (scale, zero_point).
_SUFFIXES = ('/filter1', '/filter2')
_PER_CHANNEL_OP_NAMES = (
    f'{op}{suffix}'
    for op, suffix in itertools.product(_PER_CHANNEL_QUANTIZED_OPS, _SUFFIXES)
)


def _is_variable(node_def: node_def_pb2.NodeDef) -> bool:
  """Determines whether `node_def` is a variable node.

  Args: