doc: remove section on negociation

subprojects/gst-docs/markdown/additional/design/machine-learning-analytics.md

@@ -266,77 +266,6 @@ advantage is the ability to keep a relation description between tensors in a
 refinement context On the other hand this mode of transporting analytics result
 make negotiation of tensor-decoder in particular difficult.
 
-### Negotiation
-Allowing to negotiate the required analysis pre/post-processing and automatically
-injecting the required elements to able to perform them would be very valuable and
-minimize effort of porting an analytics pipeline between different platforms and
-making use of acceleration available. Tensor-decoders bin, auto-plugging of
-pre-processing (considering acceleration available), auto-plugging of inference
-element (optimized of the platform), post-processing, tensor-decoder bin
-selecting required tensor-decoders potentially from multiple functionally
-equivalent, but more adapted to the platform are all aspect to consider when
-designing negotiation involved in analytics-pipeline.
-
-#### Negotiating Tensor-Decoder
-As described above tensor-decoder need to know 4 attributes about a tensor to
-know if it can handle it:
-
-1. Tensor dimension cardinality ( not required explicitly in some cases)
-2. Tensor dimension
-3. Tensor datatype
-4. Tensor type (identifier of analytics-result encoding semantic)
-
-Note 1, 2, 3 could be encoded into 4, but this is not desirable because 1,2,3
-are useful for selection semantically-agnostic tensor processor.
-
-Tensor-decoder can handle multiple tensor types. This could be expressed in the
-sinkpad(s) template by a list of arrays where each combination of tensor types
-it can handle would be expressed. This would make the sinkpad(s) caps difficult
-to read. To avoid this problem when a tensor-decoder handle multiple tensors the
-tensor type is a category the encapsulate all tensor type it can handle.
-Referring again to YOLOv3's 3 tensors: small, medium large, all 3 would have the
-same tensor-type identifier, ex YOLOv3, and each tensors themselves would have
-sub-type field distinguishing them ('small', 'medium', 'large'). Same also
-applies to FastSAM 2 tensors ('FastSAM-masks', 'FastSAM-logits') where both
-would be represented by the same tensor type ('FastSAM') in pad capability level.
-
-When tensor is stored as a meta, allocation query need to be used to negotiate
-tensor-decoder. TODO: expand how this would work.
-
-
-##### Tensor-Decoder Sinkpad Caps Examples
-
-Examples assuming object-detection on video frame
-```
-PadTemplates:
-  SINK template: 'vsink' // Tensor attached on to buffer
-    Avaiability: 'always'
-    Capabilities:
-      video/x-raw
-        format: {...}
-        width: {...}
-        height: {...}
-        framerate: {...}
-
-  SINK template: 'tsink' // Tensor
-    Avaiability: 'always'
-    Capabilities:
-      tensor/x-raw
-        shape:{<a, b, ...z>} // This represent a x b x ... x z
-        datatype: {(enum) "int8", "float32", ...}
-        type: { (string)"YOLOv3", (string)"YOLOv4", (string)"SSD", ...)}
-
-```
-
-##### Tensor-Decoder Srcpad(s)
-Typically will be the same as sinkpad but could be different. In general
-tensor-decoder only attach an analytics-meta to buffer. Analytics-meta
-consumption is left to other downstream elements. It's also possible for
-tensor-decoder to have very different caps on srcpad. This can be the case when
-analytics-result is difficult to represent like text-to-speech or
-super-resolution. In these case the tensor-decoder could be producing a media
-directly. audio for TTS or image for super-resolution.
-
 ### Inference Sinkpad(s) Capabilities
 Sinkpad capability, before been constrained based on model, can be any
 media type, including `tensor` . Note that multiple sinkpads can be present.