import torch
from transformers import DistilBertTokenizer, DistilBertModel

# Load the tokenizer and model
tokenizer = DistilBertTokenizer.from_pretrained("tokenizer_config.json")
model = DistilBertModel.from_pretrained("pytorch_model.bin")

# Define the inference function
def predict(text):
    # Tokenize the input
    inputs = tokenizer(text, padding="max_length", truncation=True, return_tensors="pt")

    # Perform the inference
    with torch.no_grad():
        outputs = model(**inputs)
        logits = outputs.logits

    # Convert logits to probabilities
    probabilities = torch.softmax(logits, dim=1).squeeze().tolist()

    return probabilities

# Example usage
text = "This is a sample input."
probabilities = predict(text)
print(probabilities)