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[deps]
CUDA = "052768ef-5323-5732-b1bb-66c8b64840ba"
Yao = "5872b779-8223-5990-8dd0-5abbb0748c8c"
Zygote = "e88e6eb3-aa80-5325-afca-941959d7151f"

[compat]
CUDA = "5"
Yao = "0.9.3"
Zygote = "0.7"
julia = "1.11"

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#!/bin/bash
# the script is fully created by Google Gemini
# Function to display usage information
usage() {
  echo "Usage: $0 [file_extension]"
  echo "  [file_extension] - Optional file extension to filter by (default: .py)"
  exit 1
}

# Default file extension

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import tensorflow as tf
import tensorflow_quantum as tfq
import cirq
import sympy
import numpy as np

nwires, nlayers = 6, 3
qubits = [cirq.GridQubit(0, i) for i in range(nwires)]
symbols = sympy.symbols("params_0:" + str(nlayers * nwires * 2))

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import tensorflow as tf
## note tape.watch() cannot accept ragged tensor as input
rt = tf.ragged.constant([[1.], [2., 3.]])
# rt = rt.to_tensor()
with tf.GradientTape() as t:
    t.watch(rt.values)
    loss = 2*rt[0,0]+rt[1,1]**3
g_flatten = t.gradient(loss, rt.values)
g = rt.with_values(g_flatten)
print(g)

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import xalpha as xa
import pandas as pd
import datetime as dt

def _percent(r):
    return round((r-1)*100, 2)

def index_analysis(code, start, end):
    dfe = xa.get_daily("peb-"+code, end=end, prev=20).iloc[-1]
    end = dfe["date"].strftime("%Y%m%d")

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@tf.custom_gradient
def idn(x):
    def grad(*dx):
        return tf.zeros_like(dx[0])
    return x, grad

def probexp(beta, s):
    return tf.exp(-beta*s)

def obss(beta, s):

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#! /bin/bash

ips=("8.8.8.8"
     "baidu.com")

webs=("http://10.0.0.1:8080")

address="user@mails.domain"

for ip in "${ips[@]}"; do