Example code reports that the model is overloaded

I ran the Romeo & Juliet example code, which failed after about 2 minutes with this error: `langextract.inference.InferenceOutputError: Parallel inference error: Gemini API error: 503 UNAVAILABLE. {'error': {'code': 503, 'message': 'The model is overloaded. Please try again later.', 'status': 'UNAVAILABLE'}}`

```
# /// script
# requires-python = ">=3.13"
# dependencies = [
# "langextract",
# ]
# ///
import langextract as lx
import textwrap
from collections import Counter, defaultdict

# Define comprehensive prompt and examples for complex literary text
prompt = textwrap.dedent("""\
Extract characters, emotions, and relationships from the given text.

Provide meaningful attributes for every entity to add context and depth.

Important: Use exact text from the input for extraction_text. Do not paraphrase.
Extract entities in order of appearance with no overlapping text spans.

Note: In play scripts, speaker names appear in ALL-CAPS followed by a period.""")

examples = [
lx.data.ExampleData(
text=textwrap.dedent("""\
ROMEO. But soft! What light through yonder window breaks?
It is the east, and Juliet is the sun.
JULIET. O Romeo, Romeo! Wherefore art thou Romeo?"""),
extractions=[
lx.data.Extraction(
extraction_class="character",
extraction_text="ROMEO",
attributes={"emotional_state": "wonder"}
),
lx.data.Extraction(
extraction_class="emotion",
extraction_text="But soft!",
attributes={"feeling": "gentle awe", "character": "Romeo"}
),
lx.data.Extraction(
extraction_class="relationship",
extraction_text="Juliet is the sun",
attributes={"type": "metaphor", "character_1": "Romeo", "character_2": "Juliet"}
),
lx.data.Extraction(
extraction_class="character",
extraction_text="JULIET",
attributes={"emotional_state": "yearning"}
),
lx.data.Extraction(
extraction_class="emotion",
extraction_text="Wherefore art thou Romeo?",
attributes={"feeling": "longing question", "character": "Juliet"}
),
]
)
]

# Process Romeo & Juliet directly from Project Gutenberg
print("Downloading and processing Romeo and Juliet from Project Gutenberg...")

result = lx.extract(
text_or_documents="https://www.gutenberg.org/files/1513/1513-0.txt",
prompt_description=prompt,
examples=examples,
model_id="gemini-2.5-flash",
extraction_passes=3, # Multiple passes for improved recall
max_workers=20, # Parallel processing for speed
max_char_buffer=1000 # Smaller contexts for better accuracy
)
```

Start by running the provided Romeo & Juliet script with model_id="gemini-2.5-flash", extraction_passes=3, max_workers=20, and max_char_buffer=1000, and observe the reported 503 response. The issue does not name a file, test, or requested change, so completion would first require clarifying the expected handling of an overloaded Gemini API model.

Written by the indexing model from the issue text.