⚡ Live Sandbox: AI Dev Tool: Benchmarking PocketScale Inference

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```python
# AI Dev Tool: Pocket-Scale Inference Benchmarking CLI
# Description: A command-line interface for benchmarking pocket-scale inference models
# Requirements: Python 3.9+, torch, torchvision, numpy

import torch
import torchvision
import numpy as np
import argparse
import time
from typing import Dict, List

def benchmark_model(model: torch.nn.Module, input_shape: List[int], num_runs: int = 100) -> Dict[str, float]:
    """
    Benchmark a given model on a specific input shape.

Args:
    - model (torch.nn.Module): The model to benchmark.
    - input_shape (List[int]): The shape of the input data.
    - num_runs (int): The number of runs to perform. Defaults to 100.

Returns:
    - Dict[str, float]: A dictionary containing the benchmark results.
    """
    # Initialize the input tensor
    input_tensor = torch.randn(input_shape)

# Initialize the results dictionary
    results = {"latency": 0.0, "throughput": 0.0}

# Perform the benchmark runs
    for _ in range(num_runs):
        # Start the timer
        start_time = time.time()

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