Read TF version from poetry.lock

[HotThoughts]

The problem:

We were using sed-style to read TF version from pyproject.toml, which leads us to problems when the version constraints aren't cover in the sed command. We need to find a way to read TensorFlow version from poetry.lock directly.

Proposed changes:

Use python's toml library to parse and read TensorFlow version from poetry.lock.
It now uses the new read_tenforflow_version.sh script to retrieve tf version (e.g. 2.6) and CUDA/cuDNN libraries successfully load.

[github-actions]

Hey @HotThoughts! 👋 To run model regression tests, comment with the /modeltest command and a configuration.

Tips 💡: The model regression test will be run on push events. You can re-run the tests by re-add status:model-regression-tests label or use a Re-run jobs button in Github Actions workflow.

Tips 💡: Every time when you want to change a configuration you should edit the comment with the previous configuration.

You can copy this in your comment and customize:

/modeltest

```yml
##########
## Available datasets
##########
# - "Carbon Bot" (NLU)
# - "Hermit" (NLU)
# - "Private 1" (NLU)
# - "Private 2" (NLU)
# - "Private 3" (NLU)
# - "Sara" (NLU, Core)
# - "financial-demo" (NLU, Core)
# - "helpdesk-assistant" (NLU, Core)
# - "insurance-demo" (NLU, Core)
# - "retail-demo" (NLU, Core)

##########
## Available NLU configurations
##########
# - "BERT + DIET(bow) + ResponseSelector(bow)"
# - "BERT + DIET(seq) + ResponseSelector(t2t)"
# - "Spacy + DIET(bow) + ResponseSelector(bow)"
# - "Spacy + DIET(seq) + ResponseSelector(t2t)"
# - "Sparse + BERT + DIET(bow) + ResponseSelector(bow)"
# - "Sparse + BERT + DIET(seq) + ResponseSelector(t2t)"
# - "Sparse + DIET(bow) + ResponseSelector(bow)"
# - "Sparse + DIET(seq) + ResponseSelector(t2t)"
# - "Sparse + Spacy + DIET(bow) + ResponseSelector(bow)"
# - "Sparse + Spacy + DIET(seq) + ResponseSelector(t2t)"

##########
## Available Core configurations
##########
# - "Rules"
# - "Rules + AugMemo"
# - "Rules + AugMemo + TED"
# - "Rules + Memo"
# - "Rules + Memo + TED"
# - "Rules + TED"

## Example configuration
#################### syntax #################
## include:
##   - dataset: ["<dataset_name>"]
##     config: ["<configuration_name>"]
#
## Example:
## include:
##  - dataset: ["Carbon Bot"]
##    config: ["Sparse + DIET(bow) + ResponseSelector(bow)"]
#
## Shortcut:
## You can use the "all" shortcut to include all available configurations or datasets
#
## Example: Use the "Sparse + EmbeddingIntent + ResponseSelector(bow)" configuration
## for all available datasets
## include:
##  - dataset: ["all"]
##    config: ["Sparse + DIET(bow) + ResponseSelector(bow)"]
#
## Example: Use all available configurations for the "Carbon Bot" and "Sara" datasets
## and for the "Hermit" dataset use the "Sparse + DIET + ResponseSelector(T2T)" and
## "BERT + DIET + ResponseSelector(T2T)" configurations:
## include:
##  - dataset: ["Carbon Bot", "Sara"]
##    config: ["all"]
##  - dataset: ["Hermit"]
##    config: ["Sparse + DIET(seq) + ResponseSelector(t2t)", "BERT + DIET(seq) + ResponseSelector(t2t)"]
#
## Example: Define a branch name to check-out for a dataset repository. Default branch is 'main'
## dataset_branch: "test-branch"
## include:
##  - dataset: ["Carbon Bot", "Sara"]
##    config: ["all"]
##
## Shortcuts:
## You can use the "all" shortcut to include all available configurations or datasets.
## You can use the "all-nlu" shortcut to include all available NLU configurations or datasets.
## You can use the "all-core" shortcut to include all available core configurations or datasets.

include:
 - dataset: ["Carbon Bot"]
   config: ["Sparse + DIET(bow) + ResponseSelector(bow)"]

```

[github-actions]

/modeltest

include:
 - dataset: ["Carbon Bot"]
   config: ["Sparse + DIET(bow) + ResponseSelector(bow)"]

[github-actions]

The model regression tests have started. It might take a while, please be patient.
As soon as results are ready you'll see a new comment with the results.

Used configuration can be found in the comment.

[github-actions]

Commit: 51c17fb, The full report is available as an artifact.

Dataset: Carbon Bot, Dataset repository branch: main, commit: 624f54ebc82536b144d8eebf40c27369c93fa99d

Configuration	Intent Classification Micro F1	Entity Recognition Micro F1	Response Selection Micro F1
Sparse + DIET(bow) + ResponseSelector(bow) test: 50s, train: 2m27s, total: 3m17s	0.7515 (0.00)	0.7529 (0.00)	0.5099 (0.00)

[tczekajlo]

LGTM