(len(matches2) == 1 && matches2[0][1] != fmt.Sprint(length) && ok) {
					alterColumn = true
				}
			}
		}

		// check precision
		if precision, _, ok := columnType.DecimalSize(); ok && int64(field.Precision) != precision {
			if regexp.MustCompile(fmt.Sprintf("[^0-9]%d[^0-9]", field.Precision)).MatchString(m.DataTypeOf(field)) {
				alterColumn = true
			}
		}
	}

	// check nullable

      lhs_batching_dimensions = [0],
      lhs_contracting_dimensions = [1, 2],
      rhs_batching_dimensions = [0],
      rhs_contracting_dimensions = [1, 3]
    >,
    precision_config = [#mhlo<precision DEFAULT>, #mhlo<precision DEFAULT>]
  } : (tensor<3x2x6x5x1xf32>, tensor<3x2x4x6xf32>) -> tensor<3x5x1x4xf32>
  func.return %0 : tensor<3x5x1x4xf32>

// CHECK-LABEL:   convert_dot_general

	struct { A int; B float }	// change of type for B
	struct { }			// no field names in common
	struct { C, D int }		// no field names in common

Integers are transmitted two ways: arbitrary precision signed integers or
arbitrary precision unsigned integers. There is no int8, int16 etc.
discrimination in the gob format; there are only signed and unsigned integers. As
described below, the transmitter sends the value in a variable-length encoding;

    // CHECK-NOT: tf.CustomAggregator
    %0 = stablehlo.dot_general %arg0, %arg1, contracting_dims = [1] x [0], precision = [DEFAULT, DEFAULT] : (tensor<?x100352xf32>, tensor<100352x10xf32>) -> tensor<?x10xf32>
    return %0 : tensor<?x10xf32>
  }
}

// -----

    %cst = stablehlo.constant dense<0.000000e+00> : tensor<10x1x3xf32>
    %0 = stablehlo.dot_general %arg0, %arg1, batching_dims = [0] x [0], contracting_dims = [2] x [1], precision = [DEFAULT, DEFAULT] {mhlo.frontend_attributes = {grad_x = "false", grad_y = "false"}} : (tensor<10x1x1024xf32>, tensor<10x1024x3xf32>) -> tensor<10x1x3xf32>
    %1 = stablehlo.maximum %0, %cst : tensor<10x1x3xf32>

	}

	if num, ok := field.TagSettings["SIZE"]; ok {
		if field.Size, err = strconv.Atoi(num); err != nil {
			field.Size = -1
		}
	}

	if p, ok := field.TagSettings["PRECISION"]; ok {
		field.Precision, _ = strconv.Atoi(p)
	}

	if s, ok := field.TagSettings["SCALE"]; ok {
		field.Scale, _ = strconv.Atoi(s)
	}

	// default value is function or null or blank (primary keys)

                    dependencyResultSerializer.write(encoder, dependency);
                    if (dependency.getFailure() != null) {
                        //by keying the failures only by 'requested' we lose some precision
                        //at edge case we'll lose info about a different exception if we have different failure for the same requested version
                        failures.put(dependency.getRequested(), dependency.getFailure());

                return;
            }

            // If the dependency has artifacts, only map the coordinates to component-level precision.
            // This is so we match the Gradle behavior where an explicit artifact on a dependency
            // that would otherwise map to different coordinates resolves to the declared coordinates.

  // A serialized "QuantizationInfo" object to specify value ranges for some of
  // the tensors with known names.
  std::string serialized_quant_stats = "";

  // A bitmask to encode support for reduced precision inference in the model.
  tflite::optimize::ReducedPrecisionSupport support_mask =
      tflite::optimize::ReducedPrecisionSupport::None;

  // Whether to run the passes to propagate the quantization parameters and

	dur := pd.AsDuration()
	if dur < time.Millisecond {
		return errors.New("duration must be greater than 1ms")
	}
	if dur%time.Millisecond != 0 {
		return errors.New("only durations to ms precision are supported")
	}
	return nil
}

// ValidateDurationRange verifies range is in specified duration
func ValidateDurationRange(dur, min, max time.Duration) error {
	if dur > max || dur < min {