auto qtype =
      mlir::cast<TensorType>(dq_op.getArg().getType()).getElementType();
  if (auto uniform_type = llvm::dyn_cast_or_null<UniformQuantizedType>(qtype)) {
    return uniform_type.getZeroPoint() == 0;
  } else if (auto per_axis_type =
                 llvm::dyn_cast_or_null<UniformQuantizedPerAxisType>(qtype)) {
    return absl::c_all_of(per_axis_type.getZeroPoints(),

      CreateI8F32UniformQuantizedType(UnknownLoc::get(&ctx_), ctx_,
                                      /*scale=*/8.0, /*zero_point=*/99);

  EXPECT_EQ(quantized_type.getScale(), 8.0);
  EXPECT_EQ(quantized_type.getZeroPoint(), 99);
}

class CreateI32F32UniformQuantizedTypeTest : public Test {
 protected:
  CreateI32F32UniformQuantizedTypeTest() : ctx_() {
    ctx_.loadDialect<quant::QuantizationDialect>();
  }

    auto q_type = mlir::dyn_cast_or_null<quant::UniformQuantizedType>(
        input_type.getElementType());
    if (!q_type) return failure();

    const float scale = q_type.getScale();
    const float zp = q_type.getZeroPoint();

    auto input_values = input_dequant.getValue();

    // mapValues always takes a function returning APInt, even when the output
    // is actually float.

 public:
  explicit UniformQuantizedValueConverter(
      quant::UniformQuantizedType uniform_type)
      : UniformQuantizedValueConverter(
            uniform_type.getScale(),
            static_cast<double>(uniform_type.getZeroPoint()),
            static_cast<double>(uniform_type.getStorageTypeMin()),
            static_cast<double>(uniform_type.getStorageTypeMax()),
            uniform_type.getStorageTypeIntegralWidth(),

  };
  if (auto q_type = dyn_cast<UniformQuantizedType>(type)) {
    const double scale = recalculate_scale(q_type.getScale());
    const double zero_point = recalculate_zero_point(q_type.getZeroPoint());
    return UniformQuantizedType::get(q_type.getFlags(), q_type.getStorageType(),
                                     q_type.getExpressedType(), scale,
                                     zero_point, qmin, qmax);

    new_filter_quantized_type = CreateI8F32UniformQuantizedType(
        filter_constant_op->getLoc(), *rewriter.getContext(),
        filter_quantized_type.getScale(), filter_quantized_type.getZeroPoint(),
        /*narrow_range=*/true);
  }

  // Required because the quantized dimension is changed from 3 -> 0.
  auto new_filter_result_type = RankedTensorType::getChecked(

      return quant::UniformQuantizedType::get(
          qtype.getFlags(), qtype.getStorageType(), qtype.getExpressedType(),
          qtype.getScale() * scale, qtype.getZeroPoint(),
          qtype.getStorageTypeMin(), qtype.getStorageTypeMax());
    }
    return {};
  };
}

// Returns quantization spec for LSTMs based on their operator properties.
template <typename LstmOp>

      new_qtype = quant::UniformQuantizedType::getChecked(
          op.getLoc(), flags, qtype.getStorageType(), qtype.getExpressedType(),
          uqtype.getScale(), uqtype.getZeroPoint() - offset,
          uqtype.getStorageTypeMin() - offset,
          uqtype.getStorageTypeMax() - offset);
    } else if (auto aqtype = qtype.template dyn_cast<

  zero_point = rewriter.create<TF::ConstOp>(
      loc, zero_point_type,
      DenseIntElementsAttr::get(zero_point_type,
                                {static_cast<int32_t>(qtype.getZeroPoint())}));
  return success(scale && zero_point);
}

LogicalResult CreateUniformQuantizedPerAxisTypeParams(
    quant::UniformQuantizedPerAxisType qtype, Location loc,

          new_qtype = mlir::quant::UniformQuantizedType::get(
              per_tensor.getFlags(), per_tensor.getStorageType(),
              per_tensor.getExpressedType(), per_tensor.getScale(),
              per_tensor.getZeroPoint(), per_tensor.getStorageTypeMin() - 1,
              per_tensor.getStorageTypeMax());
        } else {
          return;  // Should not reach here, as it's already checked.
        }