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Two input version of the broadcast operator to handle dynamic shapes
Added comments to describe the versions of the broadcast operator
Dynamic broadcast only handles broadcasting a static 1D shape tensor into the other input shape

Trying to keep the PRs separate

It doesn't look like this functionality is used. Probably was a thing in
ONNX that became deprecated when they adopted NumPy-like broadcasting.

change to shape::operator== makes it so this will work the same

Local Threads of multiples 32 were introduced in #1348
But LocalThreads that are not multiple of 64 are causing correctness issues.

Upgraded Dockerfiles and fixed tidy issues to make Ubuntu 20.04 and ROCm 5.3.0 the default 

Merge branch 'dyn_ref_multibroadcast' of github.com:ROCmSoftwarePlatform/AMDMIGraphX into dyn_ref_multibroadcast

updated GPU pad to now use JIT version.
added range functions for JIT kernels.

Fixes an observed regression error on certain Frozen Protobuf models due to PR 1280

use_dynamic_same_auto_pad was removed from convolution, but the driver models still retain the fields. This PR regenerates the files so that they are compatible again.

Reiterate the assertion on the standard shape but relax it for the multibroadcast ops deliberately inserted to explicit the broadcast.

update multibroadcast shape tests

Refactor dynamic compute
- add a compute_output_shape object that implicitly converts to a new dyn_output or shape object
- dyn_output object can handle computing the static output shape of an operator given the input arguments shapes
  change an operator's compute function to argument compute(const dyn_output& dyn_out, std::vector<argument> args) to 
  use dyn_output object

Dynamic ref unary functions
-  Included these changes to have an example of the refactored dynamic compute being used
-  Changes to unary base class to handle dynamic shapes
-  Changed elu and leaky_relu to use unary base class and pointwise JIT