[Bug-fix:] QEFFAutoModelForImageTextToText class docstring fixing (quic#372)

This pull request addresses the issue with the code block in the class
docstring of `QEFFAutoModelForImageTextToText`. Previously, the
docstring was not displaying correctly on `gh-pages` due to an error in
the Python code block.

---------

Signed-off-by: Abukhoyer Shaik <[email protected]>

Author: abukhoy

tests/transformers/spd/test_pld_inference.py

@@ -145,9 +145,9 @@ def get_padded_input_len(input_len: int, prefill_seq_len: int, ctx_len: int):
     """
     num_chunks = -(input_len // -prefill_seq_len)  # ceil divide without float
     input_len_padded = num_chunks * prefill_seq_len  # Convert input_len to a multiple of prefill_seq_len
-    assert (
-        input_len_padded <= ctx_len
-    ), "input_len rounded to nearest prefill_seq_len multiple should be less than ctx_len"
+    assert input_len_padded <= ctx_len, (
+        "input_len rounded to nearest prefill_seq_len multiple should be less than ctx_len"
+    )
     return input_len_padded
 
 

tests/transformers/spd/test_spd_inference.py

@@ -86,9 +86,9 @@ def get_padded_input_len(input_len: int, prefill_seq_len: int, ctx_len: int):
     """
     num_chunks = -(input_len // -prefill_seq_len)  # ceil divide without float
     input_len_padded = num_chunks * prefill_seq_len  # Convert input_len to a multiple of prefill_seq_len
-    assert (
-        input_len_padded <= ctx_len
-    ), "input_len rounded to nearest prefill_seq_len multiple should be less than ctx_len"
+    assert input_len_padded <= ctx_len, (
+        "input_len rounded to nearest prefill_seq_len multiple should be less than ctx_len"
+    )
     return input_len_padded
 
 
@@ -335,9 +335,9 @@ def test_spec_decode_inference(
     for prompt, generation in zip(prompts, batch_decode):
         print(f"{prompt=} {generation=}")
     # validation check
-    assert mean_num_accepted_tokens == float(
-        num_speculative_tokens + 1
-    ), f"mean number of accepted tokens is {mean_num_accepted_tokens} but should be {num_speculative_tokens + 1}"
+    assert mean_num_accepted_tokens == float(num_speculative_tokens + 1), (
+        f"mean number of accepted tokens is {mean_num_accepted_tokens} but should be {num_speculative_tokens + 1}"
+    )
     del target_model_session
     del draft_model_session
     generated_ids = np.asarray(generated_ids[0]).flatten()