* is not guaranteed to return zero when the dataset consists of the same value multiple times,
   * due to numerical errors. However, it is guaranteed never to return a negative result.
   *
   * <h3>Non-finite values</h3>
   *
   * <p>If the dataset contains any non-finite values ({@link Double#POSITIVE_INFINITY}, {@link

  return op.getFeatureGroupCount() > 1;
}

// Returns kernel output feature dimension of TFLite convolutions.
int64_t GetConvolutionKernelOutputFeatureDimension(bool is_depthwise) {
  return is_depthwise ? 3 : 0;
}

// Returns kernel input feature dimension of TFLite convolutions.
int64_t GetConvolutionKernelInputFeatureDimension(bool is_depthwise) {
  return is_depthwise ? 0 : 3;
}

---
name: TensorFlow Lite in Play Services issue
about: Use this template for issues with TensorFlow Lite in Google Play Services
labels: 'comp:lite-in-play-services'

---

**System information**
- Android Device information (use `adb shell getprop ro.build.fingerprint`
  if possible):
- TensorFlow Lite in Play Services SDK version (found in `build.gradle`):
- Google Play Services version

   *
   * <p>This is guaranteed to return zero if the dataset contains a single pair of finite values. It
   * is not guaranteed to return zero when the dataset consists of the same pair of values multiple
   * times, due to numerical errors.
   *
   * <h3>Non-finite values</h3>
   *
   * <p>If the dataset contains any non-finite values ({@link Double#POSITIVE_INFINITY}, {@link

#include "tensorflow/compiler/mlir/lite/ir/tfl_ops.h"
#include "tensorflow/compiler/mlir/lite/transforms/passes.h"
#include "tensorflow/compiler/mlir/lite/utils/lstm_utils.h"
#include "tensorflow/compiler/mlir/lite/utils/nms_utils.h"
#include "tensorflow/compiler/mlir/lite/utils/perception_ops_utils.h"
#include "tensorflow/compiler/mlir/lite/utils/tftext_utils.h"

func (x *Float) validate0() string {
	if x.form != finite {
		return ""
	}
	m := len(x.mant)
	if m == 0 {
		return "nonzero finite number with empty mantissa"
	}
	const msb = 1 << (_W - 1)
	if x.mant[m-1]&msb == 0 {
		return fmt.Sprintf("msb not set in last word %#x of %s", x.mant[m-1], x.Text('p', 0))
	}
	if x.prec == 0 {
		return "zero precision finite number"
	}
	return ""
}

	z.mode = RoundingMode((b >> 5) & 7)
	z.acc = Accuracy((b>>3)&3) - 1
	z.form = form((b >> 1) & 3)
	z.neg = b&1 != 0
	z.prec = byteorder.BeUint32(buf[2:])

	if z.form == finite {
		if len(buf) < 10 {
			return errors.New("Float.GobDecode: buffer too small for finite form float")
		}
		z.exp = int32(byteorder.BeUint32(buf[6:]))
		z.mant = z.mant.setBytes(buf[10:])
	}

	if oldPrec != 0 {
		z.mode = oldMode

    case tflite::TensorType_FLOAT16:
      return builder.getF16Type();
    case tflite::TensorType_BFLOAT16:
      return builder.getBF16Type();
    case tflite::TensorType_FLOAT32:
      return builder.getF32Type();
    case tflite::TensorType_FLOAT64:
      return builder.getF64Type();
    case tflite::TensorType_INT32:
      return builder.getIntegerType(32);
    case tflite::TensorType_UINT16:

  tflite::Int32VectorT *AsInt32Vector() {
    return type == SparseIndexVector_Int32Vector ?
      reinterpret_cast<tflite::Int32VectorT *>(value) : nullptr;
  }
  const tflite::Int32VectorT *AsInt32Vector() const {
    return type == SparseIndexVector_Int32Vector ?
      reinterpret_cast<const tflite::Int32VectorT *>(value) : nullptr;
  }
  tflite::Uint16VectorT *AsUint16Vector() {

   *
   * <p>This is guaranteed to return zero if the dataset contains a single pair of finite values. It
   * is not guaranteed to return zero when the dataset consists of the same pair of values multiple
   * times, due to numerical errors.
   *
   * <h3>Non-finite values</h3>
   *
   * <p>If the dataset contains any non-finite values ({@link Double#POSITIVE_INFINITY}, {@link