the second argument is the zero point constant (element type: int) and
      the third argument is the inverse scale constant (element type: float).
    * A tensor is dequantized using a `func::FuncOp` whose name contains
      "uniform_dequantize". The first argument is the tensor to be quantized,
      the second argument is the zero point constant (element type: int) and

// TODO(b/329300758): add file check back after the cl is fixed | FileCheck %s
// Check a few basic properties of the import-export,
// including constants retaining their shape
// and the module including the TFLite version.

func.func @main(tensor<3x2xi32>) -> tensor<3x2xi32> {
^bb0(%arg0: tensor<3x2xi32>):
  // CHECK: module attributes

import javax.annotation.Resource;

import org.apache.logging.log4j.LogManager;
import org.apache.logging.log4j.Logger;
import org.codelibs.core.lang.StringUtil;
import org.codelibs.core.net.URLUtil;
import org.codelibs.fess.Constants;
import org.codelibs.fess.app.web.base.FessSearchAction;
import org.codelibs.fess.app.web.error.ErrorAction;
import org.codelibs.fess.crawler.util.CharUtil;
import org.codelibs.fess.es.log.exentity.ClickLog;

  // time, a module may have constants that need to be restored.  To restore
  // these constants, they are looked up in the GraphDef's nodes by their name.
  // Since we may need to load many constants, we create a hash map of these
  // names to their corresponding nodes at load time in order to look them up
  // in constant time.
  tensorflow::StatusOr<const tensorflow::NodeDef*> GetGraphDefNode(

#include "textflag.h"

// Register definitions
#define Rtable	R0	// Pointer to MD5 constants table
#define Rdata	R1	// Pointer to data to hash
#define Ra	R2	// MD5 accumulator
#define Rb	R3	// MD5 accumulator
#define Rc	R4	// MD5 accumulator
#define Rd	R5	// MD5 accumulator
#define Rc0	R6	// MD5 constant
#define Rc1	R7	// MD5 constant
#define Rc2	R8	// MD5 constant
// r9, r10 are forbidden

  }
};

// Inserts a "quant.qcast" and "quant.dcast" op pair (QDQs) in place of the
// tf.FakeQyantWithMinMax{Vars|VarsPerChannel|Args}Op
// before the op being constant folded. Since the constant
// folding logic will use a "arith.constant" op to replace the
// "tf.FakeQuantWithMinMaxVarsOp", the "quant.qcast" op is used to preserve
// the quantization parameters as a TypeAttr and "quant.dcast" op used to

    if (dim.size() < 0) {
      return errors::Internal("Unknown dimension size in constant tensor ",
                              n->name());
    }
    total_elements *= dim.size();
  }

  // TODO(sanjoy): It may make sense to combine this threshold with XLA's "large
  // constant" threshold, if there is one.
  const int kSmallTensorThreshold = 16;
  return total_elements < kSmallTensorThreshold;
}

					cause = check.sprintf("constant %s overflows %s (in %s)", x.val, u, T)
				} else {
					cause = check.sprintf("cannot convert %s to type %s (in %s)", x, u, T)
				}
				return false
			}
			return true
		})
		x.mode = value // type parameters are not constants
	case x.convertibleTo(check, T, &cause):
		// non-constant conversion
		ok = true
		x.mode = value
	}

	if !ok {

// This code was written in Go based on the original C implementation.

// This is a tool needed to generate the appropriate constants needed for
// the vpmsum algorithm.  It is included to generate new constant tables if
// new polynomial values are included in the future.

package main

import (
	"bytes"
	"fmt"
	"os"
)

var blocking = 32 * 1024

		return constant.MakeFloat64(f)
	}
	return nil
}

func fitsFloat64(x constant.Value) bool {
	f, _ := constant.Float64Val(x)
	return !math.IsInf(f, 0)
}

func roundFloat64(x constant.Value) constant.Value {
	f, _ := constant.Float64Val(x)
	if !math.IsInf(f, 0) {
		return constant.MakeFloat64(f)
	}
	return nil
}

// representable checks that a constant operand is representable in the given