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		Accelerator
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		Generator
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ImageDecoder
Description
Loads and decodes and image from a file. If it canβt decode for any reason (e.g. corrupted encoded stream, invalid format, it will return an empty matrix).

The following image formats are supported :
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- BMP
- JPEG (note: Lossless JPEG support is optional)
- JPEG2000
- TIFF
- PNG
- WebP
- Portable image format (PBM, PGM, PPM, PXM, PNM) Decoded images follow a channel-last layout: (Height, Width, Channels).Β JPEG chroma upsampling method: When upsampling the chroma components by a factor of 2, the pixels are linearly interpolated so that the centers of the output pixels are 1/4 and 3/4 of the way between input pixel centers. When rounding, 0.5 is rounded down and up at alternative pixels locations to prevent bias towards larger values (ordered dither pattern). Considering adjacent input pixels A, B, and C, B is upsampled to pixels B0 and B1 so that :
B0 = round_half_down((1/4) * A + (3/4) * B) B1 = round_half_up((3/4) * B + (1/4) * C) This method, is the default chroma upsampling method in the well-established libjpeg-turbo library, also referred as βsmoothβ or βfancyβ upsampling. 
 
Input parameters
 specified_outputs_name :Β array, this parameter lets you manually assign custom names to the output tensors of a node.
 specified_outputs_name :Β array, this parameter lets you manually assign custom names to the output tensors of a node.
 encoded_stream (heterogeneous) – T1 : object, encoded stream.
 encoded_stream (heterogeneous) – T1 : object, encoded stream.
 Β Parameters :Β cluster,
Β Parameters :Β cluster,
 pixel_format : enum, pixel format. Can be one of βRGBβ, βBGRβ, or βGrayscaleβ.
 pixel_format : enum, pixel format. Can be one of βRGBβ, βBGRβ, or βGrayscaleβ.
Default value βRGBβ.
 Β training?Β :Β boolean, whether the layer is in training mode (can store data for backward).
Β training?Β :Β boolean, whether the layer is in training mode (can store data for backward).
Default value βTrueβ.
 Β lda coeff :Β float, defines the coefficient by which the loss derivative will be multiplied before being sent to the previous layer (since during the backward run we go backwards).
Β lda coeff :Β float, defines the coefficient by which the loss derivative will be multiplied before being sent to the previous layer (since during the backward run we go backwards).
Default value β1β.
 Β name (optional) :Β string, name of the node.
Β name (optional) :Β string, name of the node.
 
			Output parameters
 Β image (heterogeneous) – T2 : object, decoded image.
Β image (heterogeneous) – T2 : object, decoded image.
Type Constraints
tensor(uint8)) : Constrain input types to 8-bit unsigned integer tensor.
T2 in (tensor(uint8)) : Constrain output types to 8-bit unsigned integer tensor.
