MLCTensor (moe.platform.ios API)

java.lang.Object
- org.moe.natj.general.NativeObject
- - org.moe.natj.objc.ObjCObject
  - - apple.NSObject
    - - apple.mlcompute.MLCTensor

All Implemented Interfaces:

NSCopying, NSObject
```
public class MLCTensor
extends NSObject
implements NSCopying
```
MLCTensor
A tensor object

Nested Class Summary
- Nested classes/interfaces inherited from class apple.NSObject
  NSObject.Function_instanceMethodForSelector_ret, NSObject.Function_methodForSelector_ret

Constructor Summary

Constructors
Modifier Constructor and Description

protected MLCTensor(org.moe.natj.general.Pointer peer)

Constructors
Modifier	Constructor and Description
`protected`	`MLCTensor(org.moe.natj.general.Pointer peer)`

Method Summary

All Methods Static Methods Instance Methods Concrete Methods
Modifier and Type	Method and Description
`static boolean`	`accessInstanceVariablesDirectly()`
`static MLCTensor`	`alloc()`
`static MLCTensor`	`allocWithZone(org.moe.natj.general.ptr.VoidPtr zone)`
`static boolean`	`automaticallyNotifiesObserversForKey(java.lang.String key)`
`boolean`	`bindAndWriteDataToDevice(MLCTensorData data, MLCDevice device)` Associates the given data to the tensor.
`boolean`	`bindOptimizerDataDeviceData(NSArray<? extends MLCTensorData> data, NSArray<? extends MLCTensorOptimizerDeviceData> deviceData)` Associates the given optimizer data and device data buffers to the tensor.
`static void`	`cancelPreviousPerformRequestsWithTarget(java.lang.Object aTarget)`
`static void`	`cancelPreviousPerformRequestsWithTargetSelectorObject(java.lang.Object aTarget, org.moe.natj.objc.SEL aSelector, java.lang.Object anArgument)`
`static NSArray<java.lang.String>`	`classFallbacksForKeyedArchiver()`
`static org.moe.natj.objc.Class`	`classForKeyedUnarchiver()`
`boolean`	`copyDataFromDeviceMemoryToBytesLengthSynchronizeWithDevice(org.moe.natj.general.ptr.VoidPtr bytes, long length, boolean synchronizeWithDevice)` Copy tensor data from device memory to user specified memory
`java.lang.Object`	`copyWithZone(org.moe.natj.general.ptr.VoidPtr zone)`
`NSData`	`data()` [@property] data
`static java.lang.String`	`debugDescription_static()`
`static java.lang.String`	`description_static()`
`MLCTensorDescriptor`	`descriptor()` [@property] descriptor
`MLCDevice`	`device()` [@property] device
`static long`	`hash_static()`
`boolean`	`hasValidNumerics()` Returns a Boolean value indicating whether the underlying data has valid floating-point numerics, i.e.
`MLCTensor`	`init()`
`static NSObject.Function_instanceMethodForSelector_ret`	`instanceMethodForSelector(org.moe.natj.objc.SEL aSelector)`
`static NSMethodSignature`	`instanceMethodSignatureForSelector(org.moe.natj.objc.SEL aSelector)`
`static boolean`	`instancesRespondToSelector(org.moe.natj.objc.SEL aSelector)`
`static boolean`	`isSubclassOfClass(org.moe.natj.objc.Class aClass)`
`static NSSet<java.lang.String>`	`keyPathsForValuesAffectingValueForKey(java.lang.String key)`
`java.lang.String`	`label()` [@property] label
`static MLCTensor`	`new_objc()`
`NSArray<? extends MLCTensorData>`	`optimizerData()` [@property] optimizer buffers to use if tensor is used as a parameter
`NSArray<? extends MLCTensorOptimizerDeviceData>`	`optimizerDeviceData()` [@property] optimizer device buffers to use if tensor is used as a parameter
`static boolean`	`resolveClassMethod(org.moe.natj.objc.SEL sel)`
`static boolean`	`resolveInstanceMethod(org.moe.natj.objc.SEL sel)`
`void`	`setLabel(java.lang.String value)` [@property] label
`static void`	`setVersion_static(long aVersion)`
`static org.moe.natj.objc.Class`	`superclass_static()`
`boolean`	`synchronizeData()` Synchronize the data in host memory.
`boolean`	`synchronizeOptimizerData()` Synchronize the optimizer data in host memory.
`MLCTensor`	`tensorByDequantizingToTypeScaleBias(int type, MLCTensor scale, MLCTensor bias)` Converts a quantized tensor to a 32-bit floating-point tensor Returns a de-quantized tensor
`MLCTensor`	`tensorByDequantizingToTypeScaleBiasAxis(int type, MLCTensor scale, MLCTensor bias, long axis)` Converts a quantized tensor to a 32-bit floating-point tensor Returns a de-quantized tensor
`MLCTensor`	`tensorByQuantizingToTypeScaleBias(int type, float scale, long bias)` Converts a 32-bit floating-point tensor with given scale and a zero point Returns a quantized tensor
`MLCTensor`	`tensorByQuantizingToTypeScaleBiasAxis(int type, MLCTensor scale, MLCTensor bias, long axis)` Converts a 32-bit floating-point tensor with given scale and a zero point Returns a quantized tensor
`long`	`tensorID()` [@property] tensorID
`static MLCTensor`	`tensorWithDescriptor(MLCTensorDescriptor tensorDescriptor)` Create a MLCTensor object
`static MLCTensor`	`tensorWithDescriptorData(MLCTensorDescriptor tensorDescriptor, MLCTensorData data)` Create a MLCTensor object
`static MLCTensor`	`tensorWithDescriptorFillWithData(MLCTensorDescriptor tensorDescriptor, NSNumber fillData)` Create a MLCTensor object
`static MLCTensor`	`tensorWithDescriptorRandomInitializerType(MLCTensorDescriptor tensorDescriptor, int randomInitializerType)` Create a MLCTensor object
`static MLCTensor`	`tensorWithSequenceLengthFeatureChannelCountBatchSize(long sequenceLength, long featureChannelCount, long batchSize)` Create a MLCTensor object
`static MLCTensor`	`tensorWithSequenceLengthFeatureChannelCountBatchSizeData(long sequenceLength, long featureChannelCount, long batchSize, MLCTensorData data)` Create a MLCTensor object
`static MLCTensor`	`tensorWithSequenceLengthFeatureChannelCountBatchSizeRandomInitializerType(long sequenceLength, long featureChannelCount, long batchSize, int randomInitializerType)` Create a MLCTensor object
`static MLCTensor`	`tensorWithSequenceLengthsSortedSequencesFeatureChannelCountBatchSizeData(NSArray<? extends NSNumber> sequenceLengths, boolean sortedSequences, long featureChannelCount, long batchSize, MLCTensorData data)` Create a MLCTensor object
`static MLCTensor`	`tensorWithSequenceLengthsSortedSequencesFeatureChannelCountBatchSizeRandomInitializerType(NSArray<? extends NSNumber> sequenceLengths, boolean sortedSequences, long featureChannelCount, long batchSize, int randomInitializerType)` Create a MLCTensor object
`static MLCTensor`	`tensorWithShape(NSArray<? extends NSNumber> shape)` Create a MLCTensor object
`static MLCTensor`	`tensorWithShapeDataDataType(NSArray<? extends NSNumber> shape, MLCTensorData data, int dataType)` Create a MLCTensor object
`static MLCTensor`	`tensorWithShapeDataType(NSArray<? extends NSNumber> shape, int dataType)` Create a MLCTensor object
`static MLCTensor`	`tensorWithShapeFillWithDataDataType(NSArray<? extends NSNumber> shape, NSNumber fillData, int dataType)` Create a MLCTensor object
`static MLCTensor`	`tensorWithShapeRandomInitializerType(NSArray<? extends NSNumber> shape, int randomInitializerType)` Create a MLCTensor object
`static MLCTensor`	`tensorWithShapeRandomInitializerTypeDataType(NSArray<? extends NSNumber> shape, int randomInitializerType, int dataType)` Create a MLCTensor object
`static MLCTensor`	`tensorWithWidthHeightFeatureChannelCountBatchSize(long width, long height, long featureChannelCount, long batchSize)` Create a MLCTensor object
`static MLCTensor`	`tensorWithWidthHeightFeatureChannelCountBatchSizeData(long width, long height, long featureChannelCount, long batchSize, MLCTensorData data)` Create a MLCTensor object
`static MLCTensor`	`tensorWithWidthHeightFeatureChannelCountBatchSizeDataDataType(long width, long height, long featureChannelCount, long batchSize, MLCTensorData data, int dataType)` Create a MLCTensor object
`static MLCTensor`	`tensorWithWidthHeightFeatureChannelCountBatchSizeFillWithDataDataType(long width, long height, long featureChannelCount, long batchSize, float fillData, int dataType)` Create a MLCTensor object
`static MLCTensor`	`tensorWithWidthHeightFeatureChannelCountBatchSizeRandomInitializerType(long width, long height, long featureChannelCount, long batchSize, int randomInitializerType)` Create a MLCTensor object
`static long`	`version_static()`

Methods inherited from class apple.NSObject
accessibilityActivate, accessibilityActivationPoint, accessibilityAssistiveTechnologyFocusedIdentifiers, accessibilityAttributedHint, accessibilityAttributedLabel, accessibilityAttributedUserInputLabels, accessibilityAttributedValue, accessibilityContainerType, accessibilityCustomActions, accessibilityCustomRotors, accessibilityDecrement, accessibilityDragSourceDescriptors, accessibilityDropPointDescriptors, accessibilityElementAtIndex, accessibilityElementCount, accessibilityElementDidBecomeFocused, accessibilityElementDidLoseFocus, accessibilityElementIsFocused, accessibilityElements, accessibilityElementsHidden, accessibilityFrame, accessibilityHint, accessibilityIncrement, accessibilityLabel, accessibilityLanguage, accessibilityNavigationStyle, accessibilityPath, accessibilityPerformEscape, accessibilityPerformMagicTap, accessibilityRespondsToUserInteraction, accessibilityScroll, accessibilityTextualContext, accessibilityTraits, accessibilityUserInputLabels, accessibilityValue, accessibilityViewIsModal, addObserverForKeyPathOptionsContext, attemptRecoveryFromErrorOptionIndex, attemptRecoveryFromErrorOptionIndexDelegateDidRecoverSelectorContextInfo, autoContentAccessingProxy, awakeAfterUsingCoder, awakeFromNib, class_objc, classForCoder, classForKeyedArchiver, copy, dealloc, debugDescription, description, dictionaryWithValuesForKeys, didChangeValueForKey, didChangeValueForKeyWithSetMutationUsingObjects, didChangeValuesAtIndexesForKey, doesNotRecognizeSelector, fileManagerShouldProceedAfterError, fileManagerWillProcessPath, finalize_objc, forwardingTargetForSelector, forwardInvocation, hash, indexOfAccessibilityElement, isAccessibilityElement, isEqual, isKindOfClass, isMemberOfClass, isProxy, methodForSelector, methodSignatureForSelector, mutableArrayValueForKey, mutableArrayValueForKeyPath, mutableCopy, mutableOrderedSetValueForKey, mutableOrderedSetValueForKeyPath, mutableSetValueForKey, mutableSetValueForKeyPath, observationInfo, observeValueForKeyPathOfObjectChangeContext, performSelector, performSelectorInBackgroundWithObject, performSelectorOnMainThreadWithObjectWaitUntilDone, performSelectorOnMainThreadWithObjectWaitUntilDoneModes, performSelectorOnThreadWithObjectWaitUntilDone, performSelectorOnThreadWithObjectWaitUntilDoneModes, performSelectorWithObject, performSelectorWithObjectAfterDelay, performSelectorWithObjectAfterDelayInModes, performSelectorWithObjectWithObject, prepareForInterfaceBuilder, provideImageDataBytesPerRowOrigin_Size_UserInfo, removeObserverForKeyPath, removeObserverForKeyPathContext, replacementObjectForCoder, replacementObjectForKeyedArchiver, respondsToSelector, self, setAccessibilityActivationPoint, setAccessibilityAttributedHint, setAccessibilityAttributedLabel, setAccessibilityAttributedUserInputLabels, setAccessibilityAttributedValue, setAccessibilityContainerType, setAccessibilityCustomActions, setAccessibilityCustomRotors, setAccessibilityDragSourceDescriptors, setAccessibilityDropPointDescriptors, setAccessibilityElements, setAccessibilityElementsHidden, setAccessibilityFrame, setAccessibilityHint, setAccessibilityLabel, setAccessibilityLanguage, setAccessibilityNavigationStyle, setAccessibilityPath, setAccessibilityRespondsToUserInteraction, setAccessibilityTextualContext, setAccessibilityTraits, setAccessibilityUserInputLabels, setAccessibilityValue, setAccessibilityViewIsModal, setIsAccessibilityElement, setNilValueForKey, setObservationInfo, setShouldGroupAccessibilityChildren, setValueForKey, setValueForKeyPath, setValueForUndefinedKey, setValuesForKeysWithDictionary, shouldGroupAccessibilityChildren, superclass, validateValueForKeyError, validateValueForKeyPathError, valueForKey, valueForKeyPath, valueForUndefinedKey, willChangeValueForKey, willChangeValueForKeyWithSetMutationUsingObjects, willChangeValuesAtIndexesForKey

Methods inherited from class org.moe.natj.objc.ObjCObject
equals, toString

Methods inherited from class org.moe.natj.general.NativeObject
finalize, getPeer, getPeerPointer

Methods inherited from class java.lang.Object
clone, getClass, hashCode, notify, notifyAll, wait, wait, wait

- Constructor Detail
  - MLCTensor
```
protected MLCTensor(org.moe.natj.general.Pointer peer)
```
- Method Detail
  - accessInstanceVariablesDirectly
```
public static boolean accessInstanceVariablesDirectly()
```
  - alloc
```
public static MLCTensor alloc()
```
  - allocWithZone
```
public static MLCTensor allocWithZone(org.moe.natj.general.ptr.VoidPtr zone)
```
  - automaticallyNotifiesObserversForKey
```
public static boolean automaticallyNotifiesObserversForKey(java.lang.String key)
```
  - bindAndWriteDataToDevice
```
public boolean bindAndWriteDataToDevice(MLCTensorData data,
                                        MLCDevice device)
```
    Associates the given data to the tensor. If the device is GPU, also copies the data to the device memory. Returns true if the data is successfully associated with the tensor and copied to the device.
    The caller must guarantee the lifetime of the underlying memory of \p data for the entirety of the tensor's lifetime. For input tensors, we recommend that the bindAndwriteData method provided by MLCTrainingGraph and MLCInferenceGraph be used. This method should only be used to allocate and copy data to device memory for tensors that are typically layer parameters such as weights, bias for convolution layers, beta, gamma for normalization layers.
    
    Parameters:
    
    data - The data to associated with the tensor
    
    device - The compute device
    
    Returns:
    
    A Boolean value indicating whether the data is successfully associated with the tensor and copied to the device.
  - bindOptimizerDataDeviceData
```
public boolean bindOptimizerDataDeviceData(NSArray<? extends MLCTensorData> data,
                                           NSArray<? extends MLCTensorOptimizerDeviceData> deviceData)
```
    Associates the given optimizer data and device data buffers to the tensor. Returns true if the data is successfully associated with the tensor and copied to the device.
    The caller must guarantee the lifetime of the underlying memory of \p data for the entirety of the tensor's lifetime. The \p deviceData buffers are allocated by MLCompute. This method must be called before executeOptimizerUpdateWithOptions or executeWithInputsData is called for the training graph.
    
    Parameters:
    
    data - The optimizer data to be associated with the tensor
    
    deviceData - The optimizer device data to be associated with the tensor
    
    Returns:
    
    A Boolean value indicating whether the data is successfully associated with the tensor .
  - cancelPreviousPerformRequestsWithTarget
```
public static void cancelPreviousPerformRequestsWithTarget(java.lang.Object aTarget)
```
  - cancelPreviousPerformRequestsWithTargetSelectorObject
```
public static void cancelPreviousPerformRequestsWithTargetSelectorObject(java.lang.Object aTarget,
                                                                         org.moe.natj.objc.SEL aSelector,
                                                                         java.lang.Object anArgument)
```
  - classFallbacksForKeyedArchiver
```
public static NSArray<java.lang.String> classFallbacksForKeyedArchiver()
```
  - classForKeyedUnarchiver
```
public static org.moe.natj.objc.Class classForKeyedUnarchiver()
```
  - copyDataFromDeviceMemoryToBytesLengthSynchronizeWithDevice
```
public boolean copyDataFromDeviceMemoryToBytesLengthSynchronizeWithDevice(org.moe.natj.general.ptr.VoidPtr bytes,
                                                                          long length,
                                                                          boolean synchronizeWithDevice)
```
    Copy tensor data from device memory to user specified memory
    Before copying tensor data from device memory, one may need to synchronize the device memory for example when device is the GPU. The synchronizeWithDevice argumet can be set appropraitely to indicate this. For CPU this is ignored. If the tensor has been specified in outputs of a graph using addOutputs, synchronizeWithDevice should be set to NO. NOTE: This method should only be called once the graph that this tensor is used with has finished execution; Otherwise the results in device memory may not be up to date. synchronizeWithDevice must be set to NO when this method is called from a completion callback for GPU.
    
    Parameters:
    
    bytes - The user specified data in which to copy
    
    length - The size in bytes to copy
    
    synchronizeWithDevice - Whether to synchronize device memory if device is GPU
    
    Returns:
    
    Returns YES if success, NO if there is a failure to synchronize
  - copyWithZone
```
public java.lang.Object copyWithZone(org.moe.natj.general.ptr.VoidPtr zone)
```
    Specified by:
    
    copyWithZone in interface NSCopying
  - data
```
public NSData data()
```
    [@property] data
    The tensor data
  - debugDescription_static
```
public static java.lang.String debugDescription_static()
```
  - description_static
```
public static java.lang.String description_static()
```
  - descriptor
```
public MLCTensorDescriptor descriptor()
```
    [@property] descriptor
    The tensor descriptor
  - device
```
public MLCDevice device()
```
    [@property] device
    The device associated with this tensor.
  - hasValidNumerics
```
public boolean hasValidNumerics()
```
    Returns a Boolean value indicating whether the underlying data has valid floating-point numerics, i.e. it does not contain NaN or INF floating-point values.
  - hash_static
```
public static long hash_static()
```
  - init
```
public MLCTensor init()
```
    Overrides:
    
    init in class NSObject
  - instanceMethodForSelector
```
public static NSObject.Function_instanceMethodForSelector_ret instanceMethodForSelector(org.moe.natj.objc.SEL aSelector)
```
  - instanceMethodSignatureForSelector
```
public static NSMethodSignature instanceMethodSignatureForSelector(org.moe.natj.objc.SEL aSelector)
```
  - instancesRespondToSelector
```
public static boolean instancesRespondToSelector(org.moe.natj.objc.SEL aSelector)
```
  - isSubclassOfClass
```
public static boolean isSubclassOfClass(org.moe.natj.objc.Class aClass)
```
  - keyPathsForValuesAffectingValueForKey
```
public static NSSet<java.lang.String> keyPathsForValuesAffectingValueForKey(java.lang.String key)
```
  - label
```
public java.lang.String label()
```
    [@property] label
    A string to help identify this object.
  - new_objc
```
public static MLCTensor new_objc()
```
  - optimizerData
```
public NSArray<? extends MLCTensorData> optimizerData()
```
    [@property] optimizer buffers to use if tensor is used as a parameter
    These are the host side optimizer (momentum and velocity) buffers which developers can query and initialize
    When customizing optimizer data, the contents of these buffers must be initialized before executing optimizer update for a graph.
  - optimizerDeviceData
```
public NSArray<? extends MLCTensorOptimizerDeviceData> optimizerDeviceData()
```
    [@property] optimizer device buffers to use if tensor is used as a parameter
    These are the device side optimizer (momentum and velocity) buffers which developers can query
  - resolveClassMethod
```
public static boolean resolveClassMethod(org.moe.natj.objc.SEL sel)
```
  - resolveInstanceMethod
```
public static boolean resolveInstanceMethod(org.moe.natj.objc.SEL sel)
```
  - setLabel
```
public void setLabel(java.lang.String value)
```
    [@property] label
    A string to help identify this object.
  - setVersion_static
```
public static void setVersion_static(long aVersion)
```
  - superclass_static
```
public static org.moe.natj.objc.Class superclass_static()
```
  - synchronizeData
```
public boolean synchronizeData()
```
    Synchronize the data in host memory.
    Synchronize the data in host memory i.e. tensor.data with latest contents in device memory This should only be called once the graph that this tensor is used with has finished execution; Otherwise the results in device memory may not be up to date. NOTE: This method should not be called from a completion callback when device is the GPU.
    
    Returns:
    
    Returns YES if success, NO if there is a failure to synchronize
  - synchronizeOptimizerData
```
public boolean synchronizeOptimizerData()
```
    Synchronize the optimizer data in host memory.
    Synchronize the optimizer data in host memory with latest contents in device memory This should only be called once the graph that this tensor is used with has finished execution; Otherwise the results in device memory may not be up to date. NOTE: This method should not be called from a completion callback when device is the GPU.
    
    Returns:
    
    Returns YES if success, NO if there is a failure to synchronize
  - tensorByDequantizingToTypeScaleBias
```
public MLCTensor tensorByDequantizingToTypeScaleBias(int type,
                                                     MLCTensor scale,
                                                     MLCTensor bias)
```
    Converts a quantized tensor to a 32-bit floating-point tensor Returns a de-quantized tensor
    
    Parameters:
    
    type - The de-quantized data type. Must be MLCFloat32
    
    scale - The scale thst was used for the quantized data
    
    bias - The offset value that maps to float zero used for the quantized data
    
    Returns:
    
    A quantized tensor
  - tensorByDequantizingToTypeScaleBiasAxis
```
public MLCTensor tensorByDequantizingToTypeScaleBiasAxis(int type,
                                                         MLCTensor scale,
                                                         MLCTensor bias,
                                                         long axis)
```
    Converts a quantized tensor to a 32-bit floating-point tensor Returns a de-quantized tensor
    
    Parameters:
    
    type - The de-quantized data type. Must be MLCFloat32
    
    scale - The scale thst was used for the quantized data
    
    bias - The offset value that maps to float zero used for the quantized data
    
    axis - The dimension on which to apply per-channel quantization
    
    Returns:
    
    A quantized tensor
  - tensorByQuantizingToTypeScaleBias
```
public MLCTensor tensorByQuantizingToTypeScaleBias(int type,
                                                   float scale,
                                                   long bias)
```
    Converts a 32-bit floating-point tensor with given scale and a zero point Returns a quantized tensor
    
    Parameters:
    
    type - The quantized data type. Must be MLCDataTypeInt8, MLCDataTypeUInt8 or MLCDataTypeInt32
    
    scale - The scale to apply in quantization
    
    bias - The offset value that maps to float zero
    
    Returns:
    
    A quantized tensor
  - tensorByQuantizingToTypeScaleBiasAxis
```
public MLCTensor tensorByQuantizingToTypeScaleBiasAxis(int type,
                                                       MLCTensor scale,
                                                       MLCTensor bias,
                                                       long axis)
```
    Converts a 32-bit floating-point tensor with given scale and a zero point Returns a quantized tensor
    
    Parameters:
    
    type - The quantized data type. Must be MLCDataTypeInt8, MLCDataTypeUInt8 or MLCDataTypeInt32
    
    scale - The scale to apply in quantization
    
    bias - The offset value that maps to float zero
    
    axis - The dimension on which to apply per-channel quantization
    
    Returns:
    
    A quantized tensor
  - tensorID
```
public long tensorID()
```
    [@property] tensorID
    The tensor ID
    A unique number to identify each tensor. Assigned when the tensor is created.
  - tensorWithDescriptor
```
public static MLCTensor tensorWithDescriptor(MLCTensorDescriptor tensorDescriptor)
```
    Create a MLCTensor object
    Create a tensor object without any data
    
    Returns:
    
    A new MLCTensor object
  - tensorWithDescriptorData
```
public static MLCTensor tensorWithDescriptorData(MLCTensorDescriptor tensorDescriptor,
                                                 MLCTensorData data)
```
    Create a MLCTensor object
    Create a tensor object with a MLCTensorData object that specifies the tensor data buffer
    
    Parameters:
    
    tensorDescriptor - The tensor descriptor
    
    data - The random initializer type
    
    Returns:
    
    A new MLCTensor object
  - tensorWithDescriptorFillWithData
```
public static MLCTensor tensorWithDescriptorFillWithData(MLCTensorDescriptor tensorDescriptor,
                                                         NSNumber fillData)
```
    Create a MLCTensor object
    Create a tensor object with a MLCTensorData object that specifies the tensor data buffer
    
    Parameters:
    
    tensorDescriptor - The tensor descriptor
    
    fillData - The scalar data to fill to tensor with
    
    Returns:
    
    A new MLCTensor object
  - tensorWithDescriptorRandomInitializerType
```
public static MLCTensor tensorWithDescriptorRandomInitializerType(MLCTensorDescriptor tensorDescriptor,
                                                                  int randomInitializerType)
```
    Create a MLCTensor object
    Create a tensor object initialized with a random initializer such as Glorot Uniform.
    
    Parameters:
    
    tensorDescriptor - The tensor descriptor
    
    randomInitializerType - The random initializer type
    
    Returns:
    
    A new MLCTensor object
  - tensorWithSequenceLengthFeatureChannelCountBatchSize
```
public static MLCTensor tensorWithSequenceLengthFeatureChannelCountBatchSize(long sequenceLength,
                                                                             long featureChannelCount,
                                                                             long batchSize)
```
    Create a MLCTensor object
    Create a tensor typically used by a recurrent layer The tensor data type is MLCDataTypeFloat32.
    
    Parameters:
    
    sequenceLength - The length of sequences stored in the tensor
    
    featureChannelCount - Number of feature channels
    
    batchSize - The tensor batch size
    
    Returns:
    
    A new MLCTensor object
  - tensorWithSequenceLengthFeatureChannelCountBatchSizeData
```
public static MLCTensor tensorWithSequenceLengthFeatureChannelCountBatchSizeData(long sequenceLength,
                                                                                 long featureChannelCount,
                                                                                 long batchSize,
                                                                                 MLCTensorData data)
```
    Create a MLCTensor object
    Create a tensor typically used by a recurrent layer The tensor data type is MLCDataTypeFloat32.
    
    Parameters:
    
    sequenceLength - The length of sequences stored in the tensor
    
    featureChannelCount - Number of feature channels
    
    batchSize - The tensor batch size
    
    data - The tensor data
    
    Returns:
    
    A new MLCTensor object
  - tensorWithSequenceLengthFeatureChannelCountBatchSizeRandomInitializerType
```
public static MLCTensor tensorWithSequenceLengthFeatureChannelCountBatchSizeRandomInitializerType(long sequenceLength,
                                                                                                  long featureChannelCount,
                                                                                                  long batchSize,
                                                                                                  int randomInitializerType)
```
    Create a MLCTensor object
    Create a tensor typically used by a recurrent layer The tensor data type is MLCDataTypeFloat32.
    
    Parameters:
    
    sequenceLength - The length of sequences stored in the tensor
    
    featureChannelCount - Number of feature channels
    
    batchSize - The tensor batch size
    
    randomInitializerType - The random initializer type
    
    Returns:
    
    A new MLCTensor object
  - tensorWithSequenceLengthsSortedSequencesFeatureChannelCountBatchSizeData
```
public static MLCTensor tensorWithSequenceLengthsSortedSequencesFeatureChannelCountBatchSizeData(NSArray<? extends NSNumber> sequenceLengths,
                                                                                                 boolean sortedSequences,
                                                                                                 long featureChannelCount,
                                                                                                 long batchSize,
                                                                                                 MLCTensorData data)
```
    Create a MLCTensor object
    Create a tensor of variable length sequences typically used by a recurrent layer The tensor data type is MLCDataTypeFloat32.
    
    Parameters:
    
    sequenceLengths - An array of sequence lengths
    
    sortedSequences - A flag to indicate if the sequence lengths are sorted. If yes, they must be sorted in descending order
    
    featureChannelCount - Number of feature channels
    
    batchSize - The tensor batch size
    
    data - The tensor data
    
    Returns:
    
    A new MLCTensor object
  - tensorWithSequenceLengthsSortedSequencesFeatureChannelCountBatchSizeRandomInitializerType
```
public static MLCTensor tensorWithSequenceLengthsSortedSequencesFeatureChannelCountBatchSizeRandomInitializerType(NSArray<? extends NSNumber> sequenceLengths,
                                                                                                                  boolean sortedSequences,
                                                                                                                  long featureChannelCount,
                                                                                                                  long batchSize,
                                                                                                                  int randomInitializerType)
```
    Create a MLCTensor object
    Create a tensor of variable length sequences typically used by a recurrent layer The tensor data type is MLCDataTypeFloat32.
    
    Parameters:
    
    sequenceLengths - An array of sequence lengths
    
    sortedSequences - A flag to indicate if the sequence lengths are sorted. If yes, they must be sorted in descending order
    
    featureChannelCount - Number of feature channels
    
    batchSize - The tensor batch size
    
    randomInitializerType - The random initializer type
    
    Returns:
    
    A new MLCTensor object
  - tensorWithShape
```
public static MLCTensor tensorWithShape(NSArray<? extends NSNumber> shape)
```
    Create a MLCTensor object
    Create a tensor object without any data. The tensor data type is MLCDataTypeFloat32.
    
    Parameters:
    
    shape - The tensor shape
    
    Returns:
    
    A new MLCTensor object
  - tensorWithShapeDataDataType
```
public static MLCTensor tensorWithShapeDataDataType(NSArray<? extends NSNumber> shape,
                                                    MLCTensorData data,
                                                    int dataType)
```
    Create a MLCTensor object
    Create a tensor object with data
    
    Parameters:
    
    shape - The tensor shape
    
    data - The tensor data
    
    dataType - The tensor data type
    
    Returns:
    
    A new MLCTensor object
  - tensorWithShapeDataType
```
public static MLCTensor tensorWithShapeDataType(NSArray<? extends NSNumber> shape,
                                                int dataType)
```
    Create a MLCTensor object
    Create a tensor object without any data
    
    Parameters:
    
    shape - The tensor shape
    
    dataType - The tensor data type
    
    Returns:
    
    A new MLCTensor object
  - tensorWithShapeFillWithDataDataType
```
public static MLCTensor tensorWithShapeFillWithDataDataType(NSArray<? extends NSNumber> shape,
                                                            NSNumber fillData,
                                                            int dataType)
```
    Create a MLCTensor object
    Create a tensor object with data
    
    Parameters:
    
    shape - The tensor shape
    
    fillData - The scalar value to initialize the tensor data with
    
    dataType - The tensor data type
    
    Returns:
    
    A new MLCTensor object
  - tensorWithShapeRandomInitializerType
```
public static MLCTensor tensorWithShapeRandomInitializerType(NSArray<? extends NSNumber> shape,
                                                             int randomInitializerType)
```
    Create a MLCTensor object
    Create a tensor object initialized with a random initializer such as Glorot Uniform. The tensor data type is MLCDataTypeFloat32
    
    Parameters:
    
    shape - The tensor shape
    
    randomInitializerType - The random initializer type
    
    Returns:
    
    A new MLCTensor object
  - tensorWithShapeRandomInitializerTypeDataType
```
public static MLCTensor tensorWithShapeRandomInitializerTypeDataType(NSArray<? extends NSNumber> shape,
                                                                     int randomInitializerType,
                                                                     int dataType)
```
    Create a MLCTensor object
    Create a tensor object initialized with a random initializer such as Glorot Uniform. The tensor data type is MLCDataTypeFloat32
    
    Parameters:
    
    shape - The tensor shape
    
    randomInitializerType - The random initializer type
    
    dataType - The tensor data type
    
    Returns:
    
    A new MLCTensor object
  - tensorWithWidthHeightFeatureChannelCountBatchSize
```
public static MLCTensor tensorWithWidthHeightFeatureChannelCountBatchSize(long width,
                                                                          long height,
                                                                          long featureChannelCount,
                                                                          long batchSize)
```
    Create a MLCTensor object
    Create a NCHW tensor object with tensor data type = MLCDataTypeFloat32
    
    Parameters:
    
    width - The tensor width
    
    height - The tensor height
    
    featureChannelCount - Number of feature channels
    
    batchSize - The tensor batch size
    
    Returns:
    
    A new MLCTensor object
  - tensorWithWidthHeightFeatureChannelCountBatchSizeData
```
public static MLCTensor tensorWithWidthHeightFeatureChannelCountBatchSizeData(long width,
                                                                              long height,
                                                                              long featureChannelCount,
                                                                              long batchSize,
                                                                              MLCTensorData data)
```
    Create a MLCTensor object
    Create a NCHW tensor object with a tensor data object The tensor data type is MLCDataTypeFloat32.
    
    Parameters:
    
    width - The tensor width
    
    height - The tensor height
    
    featureChannelCount - Number of feature channels
    
    batchSize - The tensor batch size
    
    data - The tensor data
    
    Returns:
    
    A new MLCTensor object
  - tensorWithWidthHeightFeatureChannelCountBatchSizeDataDataType
```
public static MLCTensor tensorWithWidthHeightFeatureChannelCountBatchSizeDataDataType(long width,
                                                                                      long height,
                                                                                      long featureChannelCount,
                                                                                      long batchSize,
                                                                                      MLCTensorData data,
                                                                                      int dataType)
```
    Create a MLCTensor object
    Create a NCHW tensor object with a tensor data object The tensor data type is MLCDataTypeFloat32.
    
    Parameters:
    
    width - The tensor width
    
    height - The tensor height
    
    featureChannelCount - Number of feature channels
    
    batchSize - The tensor batch size
    
    data - The tensor data
    
    dataType - The tensor data type
    
    Returns:
    
    A new MLCTensor object
  - tensorWithWidthHeightFeatureChannelCountBatchSizeFillWithDataDataType
```
public static MLCTensor tensorWithWidthHeightFeatureChannelCountBatchSizeFillWithDataDataType(long width,
                                                                                              long height,
                                                                                              long featureChannelCount,
                                                                                              long batchSize,
                                                                                              float fillData,
                                                                                              int dataType)
```
    Create a MLCTensor object
    Create a NCHW tensor object initialized with a scalar value
    
    Parameters:
    
    width - The tensor width
    
    height - The tensor height
    
    featureChannelCount - Number of feature channels
    
    batchSize - The tensor batch size
    
    fillData - The scalar value to initialize the tensor data with
    
    dataType - The tensor data type
    
    Returns:
    
    A new MLCTensorData object
  - tensorWithWidthHeightFeatureChannelCountBatchSizeRandomInitializerType
```
public static MLCTensor tensorWithWidthHeightFeatureChannelCountBatchSizeRandomInitializerType(long width,
                                                                                               long height,
                                                                                               long featureChannelCount,
                                                                                               long batchSize,
                                                                                               int randomInitializerType)
```
    Create a MLCTensor object
    Create a NCHW tensor object initialized with a random initializer type. The tensor data type is MLCDataTypeFloat32
    
    Parameters:
    
    width - The tensor width
    
    height - The tensor height
    
    featureChannelCount - Number of feature channels
    
    batchSize - The tensor batch size
    
    randomInitializerType - The random initializer type
    
    Returns:
    
    A new MLCTensor object
  - version_static
```
public static long version_static()
```

Class MLCTensor

Nested Class Summary

Nested classes/interfaces inherited from class apple.NSObject

Constructor Summary

Method Summary

Methods inherited from class apple.NSObject

Methods inherited from class org.moe.natj.objc.ObjCObject

Methods inherited from class org.moe.natj.general.NativeObject

Methods inherited from class java.lang.Object

Constructor Detail

MLCTensor

Method Detail

accessInstanceVariablesDirectly

alloc

allocWithZone

automaticallyNotifiesObserversForKey

bindAndWriteDataToDevice

bindOptimizerDataDeviceData

cancelPreviousPerformRequestsWithTarget

cancelPreviousPerformRequestsWithTargetSelectorObject

classFallbacksForKeyedArchiver

classForKeyedUnarchiver

copyDataFromDeviceMemoryToBytesLengthSynchronizeWithDevice

copyWithZone

data

debugDescription_static

description_static

descriptor

device

hasValidNumerics

hash_static

init

instanceMethodForSelector

instanceMethodSignatureForSelector

instancesRespondToSelector

isSubclassOfClass

keyPathsForValuesAffectingValueForKey

label

new_objc

optimizerData

optimizerDeviceData

resolveClassMethod

resolveInstanceMethod

setLabel

setVersion_static

superclass_static

synchronizeData

synchronizeOptimizerData

tensorByDequantizingToTypeScaleBias

tensorByDequantizingToTypeScaleBiasAxis

tensorByQuantizingToTypeScaleBias

tensorByQuantizingToTypeScaleBiasAxis

tensorID

tensorWithDescriptor

tensorWithDescriptorData

tensorWithDescriptorFillWithData

tensorWithDescriptorRandomInitializerType

tensorWithSequenceLengthFeatureChannelCountBatchSize

tensorWithSequenceLengthFeatureChannelCountBatchSizeData

tensorWithSequenceLengthFeatureChannelCountBatchSizeRandomInitializerType

tensorWithSequenceLengthsSortedSequencesFeatureChannelCountBatchSizeData

tensorWithSequenceLengthsSortedSequencesFeatureChannelCountBatchSizeRandomInitializerType

tensorWithShape

tensorWithShapeDataDataType

tensorWithShapeDataType

tensorWithShapeFillWithDataDataType

tensorWithShapeRandomInitializerType

tensorWithShapeRandomInitializerTypeDataType

tensorWithWidthHeightFeatureChannelCountBatchSize

tensorWithWidthHeightFeatureChannelCountBatchSizeData

tensorWithWidthHeightFeatureChannelCountBatchSizeDataDataType

tensorWithWidthHeightFeatureChannelCountBatchSizeFillWithDataDataType

tensorWithWidthHeightFeatureChannelCountBatchSizeRandomInitializerType

version_static