google.genai only support batch embedding which leads to rate limit errors

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#### Environment details

- Programming language: Python
- OS: MacOS
- Language runtime version: 3.11
- Package version: google-genai==1.3.0

#### Steps to reproduce

1. Using a paid API
2. Run an embeddings job (sync)

```python

from tqdm import tqdm
import time

client = genai.Client(api_key=api_key)
error_counter = 0

for _ in tqdm(range(2000)):
success = False
while not success:
try:
response = client.models.embed_content(
model='text-embedding-004',
contents='Hello world',
)
success = True
except Exception as e:
print(e)
error_counter += 1
if "429" in (str(e)):
print("sleeping....")
time.sleep(10)

```

The function `embed_content` uses the following logic

```
path = '{model}:batchEmbedContents'.format_map(request_url_dict)
```

https://github.com/googleapis/python-genai/blob/91b1d3ee85fd32e9243e3a2da4d10a763e4d0005/google/genai/models.py#L4399

## The FIX

Please implement the following for users so that they stop hitting that 150 batch limit rate

```python

import requests
import json

def embed_content(api_key, text, model="text-embedding-004"):
"""Embeds text using the Gemini API.

Args:
api_key: Your Google Cloud API key.
text: The text to embed.
model: The embedding model to use.

Returns:
The JSON response from the API, or None if there was an error.
"""
url = f"https://generativelanguage.googleapis.com/v1beta/models/{model}:embedContent?key={api_key}"
headers = {"Content-Type": "application/json"}
data = {
"model": model,
"content": {
"parts": [{"text": text}]
}
}

try:
response = requests.post(url, headers=headers, data=json.dumps(data))
response.raise_for_status() # Raise an exception for error responses
return response.json()
except requests.exceptions.RequestException as e:
print(f"Error embedding content: {e}")
return None

# Example usage:

text = "Hello world"
response = embed_content(api_key, text)

if response:
print(response)

# test the code

for _ in tqdm(range(2000)):
embed_content(api_key, text)

```

https://ai.google.dev/api/embeddings#endpoint

Making sure to follow these steps will guarantee the quickest resolution possible.

Thanks!