Merge pull request #3 from hamzaqureshi5/ds0

refact: Name Changes

Author: hamzaqureshi5

.gitignore

@@ -1,3 +1,9 @@
+# custom 
+argparse
+dist/
+build/
+*.egg-info
+
 # Byte-compiled / optimized / DLL files
 __pycache__/
 *.py[cod]

.idea/inspectionProfiles/Project_Default.xml

@@ -0,0 +1,49 @@
+<!--- Licensed to the Apache Software Foundation (ASF) under one -->
+<!--- or more contributor license agreements.  See the NOTICE file -->
+<!--- distributed with this work for additional information -->
+<!--- regarding copyright ownership.  The ASF licenses this file -->
+<!--- to you under the Apache License, Version 2.0 (the -->
+<!--- "License"); you may not use this file except in compliance -->
+<!--- with the License.  You may obtain a copy of the License at -->
+
+<!---   http://www.apache.org/licenses/LICENSE-2.0 -->
+
+<!--- Unless required by applicable law or agreed to in writing, -->
+<!--- software distributed under the License is distributed on an -->
+<!--- "AS IS" BASIS, WITHOUT WARRANTIES OR CONDITIONS OF ANY -->
+<!--- KIND, either express or implied.  See the License for the -->
+<!--- specific language governing permissions and limitations -->
+<!--- under the License. -->
+
+TVM Contributors
+================
+TVM adopts the Apache way and governs by merit. We believe that it is important to create an inclusive community where everyone can use,
+contribute to, and influence the direction of the project. We actively invite contributors who have earned the merit to be part of the development community.
+
+See the [community structure document](https://tvm.apache.org/docs/contribute/community.html) for the explanation of community structure and contribution guidelines.
+
+
+## Committers
+
+We add tag along with committer name to show areas that they are familiar with.
+We do encourage everyone to work anything they are interested in.
+
+- [Hamza Qureshi](https://github.com/hamzaqureshi5): @hamzaqureshi5
+
+## Reviewers
+
+- [Hamza Qureshi](https://github.com/hamzaqureshi5): @hamzaqureshi5
+
+## List of Contributors
+- [Full List of Contributors](https://github.com/open-etsi/gsm-data-generator/graphs/contributors)
+
+## Mentors
+
+TVM is now a top-level Apache project. During our Incubator phase, we were fortunate to have the following mentors.
+
+- Markus Weimer @markusweimer
+- Sebastian Schelter @sscdotopen
+- Byung-Gon Chun @bgchun
+- Henry Saputra @hsaputra
+- Timothy Chen @tnachen
+- Furkan KAMACI @kamaci

.idea/inspectionProfiles/profiles_settings.xml

@@ -178,16 +178,16 @@
    APPENDIX: How to apply the Apache License to your work.
 
       To apply the Apache License to your work, attach the following
-      boilerplate notice, with the fields enclosed by brackets "[]"
+      boilerplate notice, with the fields enclosed by brackets "{}"
       replaced with your own identifying information. (Don't include
       the brackets!)  The text should be enclosed in the appropriate
       comment syntax for the file format. We also recommend that a
       file or class name and description of purpose be included on the
       same "printed page" as the copyright notice for easier
       identification within third-party archives.
 
-   Copyright [yyyy] [name of copyright owner]
 
+   Copyright [yyyy] [name of c
    Licensed under the Apache License, Version 2.0 (the "License");
    you may not use this file except in compliance with the License.
    You may obtain a copy of the License at
@@ -199,3 +199,4 @@
    WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
    See the License for the specific language governing permissions and
    limitations under the License.
+

.idea/misc.xml

@@ -1,15 +1,63 @@
-# GSM Data Generator
-
-A modular GSM data generation toolkit built in Python. It supports operator-specific logic (e.g., Zong), template-based data structures, and a user-friendly GUI for simulating synthetic telecom datasets.
-
-## Features
-- Synthetic GSM data generation
-- Operator-specific templates
-- GUI support for ease of use
-- Data output in various formats
-
-## Use Cases
-- Telecom software testing
-- Data pipeline prototyping
-- Machine learning dataset creation
-# gsm-data-genration
+<!--- Licensed to the Apache Software Foundation (ASF) under one -->
+<!--- or more contributor license agreements.  See the NOTICE file -->
+<!--- distributed with this work for additional information -->
+<!--- regarding copyright ownership.  The ASF licenses this file -->
+<!--- to you under the Apache License, Version 2.0 (the -->
+<!--- "License"); you may not use this file except in compliance -->
+<!--- with the License.  You may obtain a copy of the License at -->
+
+<!---   http://www.apache.org/licenses/LICENSE-2.0 -->
+
+<!--- Unless required by applicable law or agreed to in writing, -->
+<!--- software distributed under the License is distributed on an -->
+<!--- "AS IS" BASIS, WITHOUT WARRANTIES OR CONDITIONS OF ANY -->
+<!--- KIND, either express or implied.  See the License for the -->
+<!--- specific language governing permissions and limitations -->
+<!--- under the License. -->
+
+<img src=https://raw.githubusercontent.com/apache/tvm-site/main/images/logo/tvm-logo-small.png width=128/> Open Deep Learning Compiler Stack
+==============================================
+[Documentation](https://tvm.apache.org/docs) |
+[Contributors](CONTRIBUTORS.md) |
+[Community](https://tvm.apache.org/community) |
+[Release Notes](NEWS.md)
+
+Apache TVM is a compiler stack for deep learning systems. It is designed to close the gap between the
+productivity-focused deep learning frameworks and the performance- and efficiency-focused hardware backends.
+TVM works with deep learning frameworks to provide end-to-end compilation for different backends.
+
+License
+-------
+TVM is licensed under the [Apache-2.0](LICENSE) license.
+
+Getting Started
+---------------
+Check out the [TVM Documentation](https://tvm.apache.org/docs/) site for installation instructions, tutorials, examples, and more.
+The [Getting Started with TVM](https://tvm.apache.org/docs/get_started/overview.html) tutorial is a great
+place to start.
+
+Contribute to TVM
+-----------------
+TVM adopts the Apache committer model. We aim to create an open-source project maintained and owned by the community.
+Check out the [Contributor Guide](https://tvm.apache.org/docs/contribute/).
+
+History and Acknowledgement
+---------------------------
+TVM started as a research project for deep learning compilation.
+The first version of the project benefited a lot from the following projects:
+
+- [Halide](https://github.com/halide/Halide): Part of TVM's TIR and arithmetic simplification module
+ originates from Halide. We also learned and adapted some parts of the lowering pipeline from Halide.
+- [Loopy](https://github.com/inducer/loopy): use of integer set analysis and its loop transformation primitives.
+- [Theano](https://github.com/Theano/Theano): the design inspiration of symbolic scan operator for recurrence.
+
+Since then, the project has gone through several rounds of redesigns.
+The current design is also drastically different from the initial design, following the
+development trend of the ML compiler community.
+
+The most recent version focuses on a cross-level design with TensorIR as the tensor-level representation
+and Relax as the graph-level representation and Python-first transformations.
+The project's current design goal is to make the ML compiler accessible by enabling most
+transformations to be customizable in Python and bringing a cross-level representation that can jointly
+optimize computational graphs, tensor programs, and libraries. The project is also a foundation
+infra for building Python-first vertical compilers for domains, such as LLMs.