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Bumps [tensorflow](https://github.com/tensorflow/tensorflow) from 2.5.2 to 2.5.3.
- [Release notes](https://github.com/tensorflow/tensorflow/releases)
- [Changelog](https://github.com/tensorflow/tensorflow/blob/master/RELEASE.md)
- [Commits](https://github.com/tensorflow/tensorflow/compare/v2.5.2...v2.5.3

)

---
updated-dependencies:
- dependency-name: tensorflow
  dependency-type: direct:production
...
Signed-off-by: dependabot[bot] <support@github.com>
Co-authored-by: dependabot[bot] <49699333+dependabot[bot]@users.noreply.github.com>

Support nonstandard shapes like slice, broadcast and transpose for the unsqueeze op

* add submodule test
* remove for loop
* simplify reshape test

Update copyright for a python file that was modified when added to our code 

There is now a MIGRAPHX_DISABLE_POINTWISE_FUSION to disable it

Support slice, broadcast and transpose shapes for the squeeze op.

Changed MessagePack file extensions to mxr.

This causes incorrect memory coloring, which was causing the accuracy failures in the vision model when enabling the pointwise fusions. Resnet50, inceptionv3 and inceptionv4 do verify now in the driver.

Enforce types to avoid compilation error in pointwise fusions
This fixes compile failure: gpt-2, fp16 on Navi

Currently, MIGRAPHX_TRACE_EVAL=2 prints out the entire output buffer, but this can produce a lot of output. To make it easier to inspect and debug, using MIGRAPHX_TRACE_EVAL=2 now only prints 10 elements from the buffer(the first 5 and last 5) and shows any fp classifications found in the buffer(ie nans, infinity, etc). The previous behavior can still be enabled with MIGRAPHX_TRACE_EVAL=3.

This will also check the types using mypy on the CI.

* use the parse_resize to parse the upsample operator

Add cppcheck to examples

* Enable auto vectorization
* Handle vector types with convert function
* Dont vectorize when it will cause problems with preload

* Add mean op onnx parser and unit tests
* Refactor parse_mean to use add_broadcastable_binary_op

allow nonstd shape for the arg ops, non-standard shapes include broadcast, slice and transpose

Add HardSwish to HardSigmoid parser

HardSwish formula is y = x * HardSigmoid<alpha=1/6, beta=0.5>(x)
HardSigmoid parser sets alpha to 1/6 and adds the mul instruction if op name is HardSwish

Resolves #1062

Add onnx parser for operator GreaterOrEqual

Add onnx parser and unit tests for Softsign

* Add onnx parser and unit test

* Improve handling of generator expressions when getting the flags for hip

There have been hangs in the CI runs recently.  Github runner jobs
are failing due to exceeding file system size.  Upgrading to 0.0.11
resolves this issue.

Make clip a pointwise op

Add HardSigmoid onnx parser and unit tests
Produces mathematical equivalent to ONNX operator through combination of existing pointwise ops.
Resolves #1028

* Add matcher for conv_bias pointwise
* Add fusion op

Fix bug caused by casting time seed to float

nfnet update
3dunet requirements via pip
3dunet requirement and nb-clean

Changed the number of threads in a block from 256 to 128
Increased the max number of blocks in the kernel from 256 to 1M.
For the case that the axis is the last dimension, we removed the computation of index since it is not required.

With these change, we can get about 2x speedup compared to the develop branch for the softmax op used in the BertSquad model.

Fuse last instruction in fuse_pointwise
This is also fixes a bug with using an invalid iterator.

simple variable rename

1. Previous implementation assumes inputs and outputs .pb files are ordered, but it is not the case. So, we should use the name of the tensors in the input/output .pb files to match the input and output in the onnx model. (This change applies to the BERT_Squad model)
2. When parsing a model with dynamic input shape, current implementation uses the default batch_size for the unknown dims, which can cause parsing error for some cases (e.g. mask_rcnn model). The solution is we first read an input to get the shape, then use these shapes to parse the onnx model.

Adds description for roctx knob of migraphx-driver in documentation.

Fix pointwise compile error with half sqrt 

Fix whitespace bug in fusable_conv matcher and add unit test

Resolves a problem in parsing the ssd-10 model.

The problem is, after inserting contiguous in the auto_contiguous pass, standard output shape of some operators becomes non-standard. Then, if the next operator requires standard input shape, an exception is throw.

For example, if we pass the following model:
Input (standard shape) -> transpose (transposed) -> softmax (transposed) -> transpose (standard) -> gather.
It works fine, and no contiguous is required.

In the auto_contiguous pass, a contiguous is inserted after the first transpose. Then we need to replace the first transpose with the contiguous and recompute all shapes. When it comes to the gather operator, its input is a transposed shape, and an exception is thrown.

The solution is in the recompute_shape() function. If it is called by the auto_contiguous pass and shape of an instruction is changed, and the shape is non_standard, we do not recompute shape of its output. The reason is: since its output shape is non_standard, a contiguous op will be added after the instruction, which will recompute shape for later operators.

Bumps [tensorflow](https://github.com/tensorflow/tensorflow) from 2.5.1 to 2.5.2.
- [Release notes](https://github.com/tensorflow/tensorflow/releases)
- [Changelog](https://github.com/tensorflow/tensorflow/blob/master/RELEASE.md)
- [Commits](https://github.com/tensorflow/tensorflow/compare/v2.5.1...v2.5.2

)

---
updated-dependencies:
- dependency-name: tensorflow
  dependency-type: direct:production
...
Signed-off-by: dependabot[bot] <support@github.com>
Co-authored-by: dependabot[bot] <49699333+dependabot[bot]@users.noreply.github.com>
Co-authored-by: Chris Austen <causten@users.noreply.github.com>