}

// Grammar for Operand:
//
// operand → multOp ( ("-" | "+") multOp )*
// multOp  → unary ( ("/" | "*" | "%") unary )*
// unary   → "-" unary | primary
// primary → Value | Variable | "(" expression ")"
//

// An Operand is a single term followed by an optional sequence of
// terms separated by +/-
type Operand struct {
	Left  *MultOp     `parser:"@@"`
	Right []*OpFactor `parser:"(@@)*"`
}

		if rerr != nil {
			return nil, rerr
		}
		err := lval.arithOp(op, rval)
		if err != nil {
			return nil, err
		}
	}
	return lval, nil
}

func (e *MultOp) evalNode(r Record, tableAlias string) (*Value, error) {
	lval, lerr := e.Left.evalNode(r, tableAlias)
	if lerr != nil || len(e.Right) == 0 {
		return lval, lerr
	}

    -webkit-filter: grayscale(1);
    opacity: 0.7;
}

.multi-language-selector .language-option.selected {
    background-color: #f7f7f8;
    color: #02303a;
    filter: none;
    -webkit-filter: none;
    opacity: 1;
}

.multi-language-text.hidden,
.multi-language-selector ~ .multi-language-sample.hidden {
    display: none;
}

.multi-language-sample {
    border-radius: 0 0 4px 4px;
}

.dark-mode {

  Operation* op_to_fold = FindOperationOfType<TF::MulOp>(test_func);
  SmallVector<Value> results = ConstantFoldOpIfPossible(op_to_fold);
  EXPECT_THAT(results, SizeIs(1));
  // No constant-folding since the second operand is an argument.
  TF::MulOp mul_op = dyn_cast_or_null<TF::MulOp>(results[0].getDefiningOp());
  EXPECT_THAT(mul_op, NotNull());

${testFrameworkChoice.raw}
Project name (default: demo):
${packageNameChoice.raw}

BUILD SUCCESSFUL
2 actionable tasks: 2 executed
----

The `init` task generates the new project with the following structure:

[source.multi-language-sample,kotlin]
----
├── gradle // <1>
│   ├── libs.versions.toml // <2>
│   └── wrapper
│       ├── gradle-wrapper.jar
│       └── gradle-wrapper.properties
├── gradlew // <3>
├── gradlew.bat // <3>

John Howard <******@****.***> 1709052916 -0800

  if (!dq_op) {
    auto mul_op = builder.create<TF::MulOp>(loc, value, multiplier);
    return mul_op.getResult();
  }
  auto q_op = dq_op.getArg().getDefiningOp<quantfork::QuantizeCastOp>();
  if (!q_op) return {};

  Value float_value = q_op.getArg();
  Value new_value = builder.create<TF::MulOp>(loc, float_value, multiplier);

    // type.
    scale_op = rewriter.create<TF::CastOp>(
        loc, UnrankedTensorType::get(rewriter.getBF16Type()), scale_op);
  }

  auto mul_op = rewriter.create<TF::MulOp>(loc, new_cast_op.getType(), scale_op,
                                           new_cast_op);
  return mul_op;
}

// Add uniform quantization's dequantization logic.
std::optional<TF::PartitionedCallOp> AddUniformDequantizeOps(

    multiplier = rewriter.create<mhlo::RsqrtOp>(bn_op.getLoc(), multiplier);
    multiplier = rewriter.create<mhlo::MulOp>(bn_op.getLoc(), multiplier,
                                              bn_op.getScale());

    // Compute rhs = offset - mean * multiplier
    Value rhs = rewriter.create<mhlo::MulOp>(bn_op.getLoc(), multiplier,
                                             bn_op.getMean());

Poorna <******@****.***> 1716056341 -0700