DenseFPElementsAttr float_attr;
    if (matchPattern(value, m_Constant(&float_attr)) && float_attr.isSplat() &&
        float_attr.getSplatValue<APFloat>().isExactlyValue(raw_value))
      return true;
  } else if (mlir::isa<IntegerType>(element_type)) {
    DenseIntElementsAttr int_attr;
    if (matchPattern(value, m_Constant(&int_attr)) && int_attr.isSplat() &&
        int_attr.getSplatValue<APInt>() == raw_value)

  LogicalResult matchAndRewrite(TFL::BatchMatMulOp bmm_op,
                                PatternRewriter& rewriter) const override {
    DenseElementsAttr constant;
    if (auto rhs = bmm_op.getY(); !matchPattern(rhs, m_Constant(&constant))) {
      // The constant may be preceded by QDQs in models with QDQ format, so we
      // should set it to the real constant.
      auto dq = dyn_cast_or_null<DequantizeOp>(rhs.getDefiningOp());

      id2.replaceAllUsesWith(id2.getInput());
      max = tf_op.getMax();
      rewriter.eraseOp(id2);
    }
    if (!matchPattern(min, m_Constant(&min_value))) return failure();
    if (!matchPattern(max, m_Constant(&max_value))) return failure();

    int quant_dim = -1;
    if (PerAxis) {
      // This is a special case that the quant_dim is the last dimensions

      min = min_id.getInput();
    }
    if (auto max_id = max.getDefiningOp<TF::IdentityOp>()) {
      max = max_id.getInput();
    }

    if (!matchPattern(min, m_Constant(&min_value))) {
      return false;
    }
    if (!matchPattern(max, m_Constant(&max_value))) {
      return false;
    }
    return true;  // Successfully matched and fetched.
  }
};

    if (!matchPattern(reduce_op.getInitValues().front(),
                      m_Constant(&operand_init)))
      return failure();
    if (!IsValueInitValue(operand_init)) return failure();

    // Check that the iota init is zero.
    DenseElementsAttr iota_init;
    if (!matchPattern(reduce_op.getInitValues().back(), m_Constant(&iota_init)))
      return failure();
    if (iota_init.getValues<APInt>()[0] != 0) return failure();

    if (matchPattern(input_op, m_Constant(&attr))) {
      // Constant case.
      builder->setInsertionPoint(op);
      Operation* duplicated_input_op = builder->clone(*input_op);

      // Rewire the inputs.
      op->setOperand(index, duplicated_input_op->getResult(0));
    } else if (auto dq = dyn_cast<DequantizeOp>(input_op);
               dq && matchPattern(dq.getInput(), m_Constant(&attr))) {

                  AttrType &max_value) const {
    Value min = tf_op.getMin(), max = tf_op.getMax();
    if (!matchPattern(min, m_Constant(&min_value))) {
      return false;
    }
    if (!matchPattern(max, m_Constant(&max_value))) {
      return false;
    }
    return true;  // Successfully matched and fetched.
  }
};

// routine uses the constant op's attribute to get the actual shape.
RankedTensorType GetRankedTensorTypeForOperand(Value operand) {
  DenseElementsAttr attr;
  if (matchPattern(operand, m_Constant(&attr))) {
    return mlir::dyn_cast<RankedTensorType>(attr.getType());
  }
  return mlir::dyn_cast<RankedTensorType>(operand.getType());
}

  auto input_type = mlir::dyn_cast<RankedTensorType>(input.getType());
  if (!input_type) return success();
  int64_t rank = input_type.getRank();

  DenseIntElementsAttr dims_attr;
  if (!matchPattern(dims, m_Constant(&dims_attr))) return success();
  for (const auto &dim_pair : llvm::enumerate(dims_attr)) {
    int64_t cur_dim = dim_pair.value().getSExtValue();
    if (cur_dim < -rank || cur_dim >= rank)
      return emitError(loc)

  Value packed_value = PackOperand(builder, module->getLoc(), value, pack_dim);
  DenseIntElementsAttr packed_value_attr;
  ASSERT_TRUE(matchPattern(packed_value, m_Constant(&packed_value_attr)));

  ShapedType packed_shape_type =
      mlir::dyn_cast<ShapedType>(packed_value.getType());
  llvm::SmallVector<int64_t> packed_shape(packed_shape_type.getShape().begin(),