// CHECK-LABEL: func private @quantized_matmul_fn_0
// CHECK:  %0 = "tf.UniformQuantizedDotHybrid"(%arg0, %arg1, %arg2, %arg3)
// CHECK-SAME: rhs_quantization_axis = -1 : i64
// CHECK-SAME: rhs_quantization_max_val = 127 : i64
// CHECK-SAME: rhs_quantization_min_val = -128 : i64

}

// -----

module {

    // Currently for dot, PTQ supports per-tensor quantization.
    if (rhs_quantized_dimension != -1) {
      return rewriter.notifyMatchFailure(
          op, "Legalization supports only rhs_quantization_axis -1.");
    }
    auto rhs_type = GetUniformQuantizedType(
        op, op.getRhs().getType(), op.getRhsScales(), op.getRhsZeroPoints(),
        /*expressed_type=*/rewriter.getF32Type(), op.getRhsQuantizationMinVal(),

Must have same shape with `output_scales`.}]>:$output_zero_points,

    DefaultValuedOptionalAttr<I64Attr, "-1">:$lhs_quantization_axis,
    I64Attr:$lhs_quantization_min_val,
    I64Attr:$lhs_quantization_max_val,
    DefaultValuedOptionalAttr<I64Attr, "-1">:$rhs_quantization_axis,
    I64Attr:$rhs_quantization_min_val,
    I64Attr:$rhs_quantization_max_val,

      self.assertEqual(
          self._count_ops(
              output_graphdef,
              _PER_CHANNEL_QUANTIZED_OPS,
              'rhs_quantization_axis',
              quantized_axis_attr,
          ),
          self._count_ops(output_graphdef, _PER_CHANNEL_QUANTIZED_OPS),
      )
      self.assertEqual(
          self._count_ops(