Add gemma4-26b E2E test scripts for pre-training

[chiajunglien]

Description

This PR introduces end-to-end (E2E) testing and validation pipelines for the Gemma 4 26B model in MaxText.
Blocked by: https://buganizer.corp.google.com/issues/527749229

Tests

Pre-Training:

export RUN_ID=$(date +%Y-%m-%d-%H-%M)
bash tests/end_to_end/tpu/gemma4/26b/test_gemma4_to_mt.sh $RUN_ID
bash tests/end_to_end/tpu/gemma4/26b/test_gemma4.sh $RUN_ID

Checklist

Before submitting this PR, please make sure (put X in square brackets):

• I have performed a self-review of my code. For an optional AI review, add the gemini-review label.
• I have necessary comments in my code, particularly in hard-to-understand areas.
• I have run end-to-end tests tests and provided workload links above if applicable.
• I have made or will make corresponding changes to the doc if needed, including adding new documentation pages to the relevant Table of Contents (toctree directive) as explained in our documentation.

[RexBearIU]

Please test whether we still need it.

[chiajunglien]

Using the command below to test:

xpk workload create \
  --cluster=mesa-v6e32-eu \
  --workload=gemma4-logit-check-$(date +%m%d%H%M) \
  --device-type=v6e-32 \
  --num-slices=1 \
  --priority=high \
  --project=cienet-cmcs \
  --zone=europe-west4-a \
  --skip-validation \
  --docker-image=gcr.io/tpu-prod-env-multipod/maxtext_jax_nightly:28210168847 \
  --command="set -xue; \
    python3 -m tests.utils.forward_pass_logit_checker \
      load_parameters_path='gs://us-central1-emmalien-test-83df3ecd-bucket/gemma4-26b-0625/unscanned/2026-06-25-06-21-12/0/items' \
      model_name=gemma4-26b \
      use_multimodal=false \
      scan_layers=false \
      --hf_model_path=google/gemma-4-26b-a4b-it \
      --max_kl_div=0.03 \
      --run_hf_model=true \
      hardware=cpu \
      skip_jax_distributed_system=True"

and got the error:

[transformers] PyTorch was not found. Models won't be available and only tokenizers, configuration and file/data utilities can be used.
[transformers] DeepseekV32Config got `key=rope_scaling` in kwargs but hasn't set it as attribute. For RoPE standardization you need to set `self.rope_parameters` in model's config. 
Traceback (most recent call last):
  File "<frozen runpy>", line 198, in _run_module_as_main
  File "<frozen runpy>", line 88, in _run_code
  File "/deps/tests/utils/forward_pass_logit_checker.py", line 50, in <module>
    from maxtext.checkpoint_conversion.utils.hf_utils import convert_jax_weight_to_torch
  File "/deps/src/maxtext/checkpoint_conversion/utils/hf_utils.py", line 25, in <module>
    import torch.nn.functional as F
ModuleNotFoundError: No module named 'torch'
XPK End: Fri Jun 26 09:08:08 UTC 2026
EXIT_CODE=1

Seems it still need torch to be installed.