//
// If the format specifier includes a %w verb with an error operand,
// the returned error will implement an Unwrap method returning the operand.
// If there is more than one %w verb, the returned error will implement an
// Unwrap method returning a []error containing all the %w operands in the
// order they appear in the arguments.
// It is invalid to supply the %w verb with an operand that does not implement

    return;
  }

  Operation* input_def = input.getDefiningOp();

  // If ShapeOp operand is a ReadVariableOp result where the ReadVariableOp
  // operand is a replicate resource block argument, replace ShapeOp with
  // VariableShapeOp and use the associated first replica operand as its
  // operand.
  auto read_var_op = llvm::dyn_cast<TF::ReadVariableOp>(input_def);
  if (!read_var_op) return;

namespace mlir {

class Operation;

namespace TF {

// Runs TensorFlow shape inference associated to the op type registered in the
// TensorFlow op registry based on the Graph version, operands, and attributes.
// Invoking this shape function will create conversions of parameters to the
// TensorFlow Graph equivalent data structures and back to MLIR equivalent data

    auto res = tf_op.getOutputs();
    if (!res.hasOneUse() || isa<quantfork::QuantizeCastOp>(*res.user_begin()))
      return failure();

    // Extract the min/max constant values from the operands. We also consider
    // a special case that there are tf.Identity ops between the min/max
    // constants and the tf.FakeQuantWithMinMaxVarsOp.
    Value min = tf_op.getMin(), max = tf_op.getMax();

		if !ok || bin.Op != op.badEq {
			continue
		}

		// In order to avoid false positives, restrict to cases
		// in which one of the operands is constant. We're then
		// interested in the other operand.
		// In the rare case in which both operands are constant
		// (e.g. runtime.GOOS and "windows"), we'll only catch
		// mistakes if the LHS is repeated, which is how most
		// code is written.
		var x ast.Expr

                                OpBuilder &rewriter) const {
    if (tf_op.getNumBits() != 8) {
      return failure();
    }

    // Extract the min/max constant values from the operands. We also consider
    // a special case that there are tf.Identity ops between the min/max
    // constants and the tf.FakeQuantWithMinMaxVarsOp.
    FetchAttrType min_value, max_value;

    auto res = tf_op.getOutputs();
    if (!res.hasOneUse() || isa<QuantizeOp>(*res.user_begin())) {
      return failure();
    }

    // Extract the min/max constant values from the operands. We also consider
    // a special case that there are tf.Identity ops between the min/max
    // constants and the tf.FakeQuantWithMinMaxVarsOp.

    FetchAttrType min_value, max_value;

// A utility class that enables the conversion of the LSTMCellSimple composite
// op into a fused TFL LSTM op. The fused op is contained within a FuncOp
// that also contains other supporting ops needed to construct the operands for
// the fused op. The caller provides the containing FuncOp as input with
// arguments specifying the input, weight, projection and bias.
// The weight, projection, bias and layer norm scale all need to be

// location with a `CallOp` which invokes said `FuncOp`. The inputs to
// this new functions are taken to be the `Values` that appear as operands
// to ops in the subgraph, which are not self-contained within the subgraph.
// The outputs of this function are taken to be the results of ops in the
// subgraph which are referenced as operands outside of the subgraph.
// Also refer to documention of `AccumulateOperandsDefinedAbove` &
// `AccumulateResultsDefinedWithin`.

  BFloat16TypePattern(TypeConverter& converter, MLIRContext* ctx)
      : ConversionPattern(converter, MatchAnyOpTypeTag(), /*benefit=*/1, ctx) {}

  LogicalResult matchAndRewrite(
      Operation* op, const ArrayRef<Value> operands,
      ConversionPatternRewriter& rewriter) const override {
    if (getTypeConverter()->isLegal(op)) {
      return failure();
    }
    if (isa<mlir::stablehlo::BitcastConvertOp>(op)) {