Tensor

Initialization for an RGB or RGBA color array tensor.

The color type (combining DataType and number of channels) used to define a color array.

Data type enums.

Initialization of a tensor for double array. If the double values to be contained by the tensor is supposed to be static, you should consider using the DoubleArray.tensor and Double.tensor defined in Pipeline, which allows you to do the following:

Initialization of a tensor for float array. If the float values to be contained by the tensor is supposed to be static, you should consider using the FloatArray.tensor and Float.tensor defined in Pipeline, which allows you to do the following:

The most fundamental initialization configuration for Tensor.

Initialization of a tensor for int array. If the int values to be contained by the tensor is supposed to be static, you should consider using the IntArray.tensor and Int.tensor defined in Pipeline, which allows you to do the following:

Initialization config to declare a multi-dimensional tensor. This tensor type follows the conventional tensor definition used in mathematics and physics applications. The data given to the tensor will be interpreted as an array of the declared data type. This is the only type that supports arithmetic operations.

Initialization for a POINT2 array tensor.

Initialization for a POINT3 array tensor.

Initialization for a SCALAR array tensor.

Initialization of a tensor for short array. If the short values to be contained by the tensor is supposed to be static, you should consider using the ShortArray.tensor and Short.tensor defined in Pipeline, which allows you to do the following:

Initialization for a SLICE array tensor.

Tensor's initialization config for special usage (usages other than the conventional multi-dimensional tensors).

Initialization of a tensor for String. If the string to be contained by the tensor is supposed to be static, you should consider using the String.tensor defined in Pipeline, which allows you to do the following:

Enum to declare tensor's usage. By default, the tensor shall all be of multi-dimensional usage, which creates non-structured data arrays. Such a tensor observes the conventional definition of tensors in linear algebra, where the values it contains are non-structured.

Initialization for a timestamp.

The content to the tensor. The provided SharedMemory shall be closed by the caller after the setter completes.

Callback when the tensor resource is reset.