(function (root, factory, undef) { if (typeof exports === "object") { // CommonJS module.exports = exports = factory(require("./core"), require("./x64-core"), require("./lib-typedarrays"), require("./enc-utf16"), require("./enc-base64"), require("./enc-base64url"), require("./md5"), require("./sha1"), require("./sha256"), require("./sha224"), require("./sha512"), require("./sha384"), require("./sha3"), require("./ripemd160"), require("./hmac"), require("./pbkdf2"), require("./evpkdf"), require("./cipher-core"), require("./mode-cfb"), require("./mode-ctr"), require("./mode-ctr-gladman"), require("./mode-ofb"), require("./mode-ecb"), require("./pad-ansix923"), require("./pad-iso10126"), require("./pad-iso97971"), require("./pad-zeropadding"), require("./pad-nopadding"), require("./format-hex"), require("./aes"), require("./tripledes"), require("./rc4"), require("./rabbit"), require("./rabbit-legacy")); } else if (typeof define === "function" && define.amd) { // AMD define(["./core", "./x64-core", "./lib-typedarrays", "./enc-utf16", "./enc-base64", "./enc-base64url", "./md5", "./sha1", "./sha256", "./sha224", "./sha512", "./sha384", "./sha3", "./ripemd160", "./hmac", "./pbkdf2", "./evpkdf", "./cipher-core", "./mode-cfb", "./mode-ctr", "./mode-ctr-gladman", "./mode-ofb", "./mode-ecb", "./pad-ansix923", "./pad-iso10126", "./pad-iso97971", "./pad-zeropadding", "./pad-nopadding", "./format-hex", "./aes", "./tripledes", "./rc4", "./rabbit", "./rabbit-legacy"], factory); } else { // Global (browser) root.CryptoJS = factory(root.CryptoJS); } }(this, function (CryptoJS) { return CryptoJS; }));解释这段代码

这段代码是一个 JavaScript 模块的定义，它通过 CommonJS 或 AMD 或全局变量的方式来暴露一个名为 CryptoJS 的对象。在模块中，它使用了一些其它模块（通过 require 或 define 引入），包括核心模块、哈希算法、加解密模块、填充模块以及格式化模块等。最终，它返回了一个 CryptoJS 对象，供其它代码使用。这段代码的目的是实现一个完整的密码学库，可以用于浏览器端或 Node.js 环境。

相关问题

把这里sys.argv传参改成用namelist的方法 import sys CASEPATH=sys.argv[1] if sys.argv[2] == "GRID": GRIDBASE="#define GRIDBASED" CATCHMENT="#undef CATCHMENT" UNSTRUCTU="#undef UNSTRUCTURED" SINGLEPOI="#undef SinglePoint"

你可以使用namelist的方法来替代sys.argv传参。首先，创建一个namelist文件，例如"input.nml"，在文件中定义你需要的参数：

&params
    CASEPATH = 'path/to/case'
    GRIDTYPE = 'GRID'
    CATCHMENT = .FALSE.
    UNSTRUCTURED = .FALSE.
    SINGLEPOINT = .FALSE.
/

然后，在你的代码中使用一个Fortran读取namelist文件的库（例如f90nml）来读取参数：

program your_program
    use f90nml

    type(params) :: input_params
    character(len=256) :: namelist_file

    namelist_file = 'input.nml'
    read(namelist_file, nml=input_params)

    CASEPATH = input_params%CASEPATH
    GRIDTYPE = input_params%GRIDTYPE
    CATCHMENT = input_params%CATCHMENT
    UNSTRUCTURED = input_params%UNSTRUCTURED
    SINGLEPOINT = input_params%SINGLEPOINT

    ! Rest of your code...
end program your_program

这样，你就可以通过修改"input.nml"文件来改变参数，而不需要使用命令行参数传递。

File "/root/Desktop/EAST-master/multigpu_train.py", line 180, in <module> tf.app.run() File "/root/miniconda3/envs/txy2/lib/python3.7/site-packages/tensorflow_core/python/platform/app.py", line 40, in run _run(main=main, argv=argv, flags_parser=_parse_flags_tolerate_undef) File "/root/miniconda3/envs/txy2/lib/python3.7/site-packages/absl/app.py", line 312, in run _run_main(main, args) File "/root/miniconda3/envs/txy2/lib/python3.7/site-packages/absl/app.py", line 258, in _run_main sys.exit(main(argv)) File "/root/Desktop/EAST-master/multigpu_train.py", line 110, in main total_loss, model_loss = tower_loss(iis, isms, igms, itms, reuse_variables) File "/root/Desktop/EAST-master/multigpu_train.py", line 30, in tower_loss f_score, f_geometry = model.model(images, is_training=True) File "/root/Desktop/EAST-master/model.py", line 77, in model spp_output = spp_layer(f[0]) File "/root/Desktop/EAST-master/model.py", line 44, in spp_layer strides=[1, strides[0], strides[1], 1], padding='VALID') File "/root/miniconda3/envs/txy2/lib/python3.7/site-packages/tensorflow_core/python/ops/nn_ops.py", line 3815, in max_pool name=name) File "/root/miniconda3/envs/txy2/lib/python3.7/site-packages/tensorflow_core/python/ops/gen_nn_ops.py", line 5662, in max_pool ksize = [_execute.make_int(_i, "ksize") for _i in ksize] File "/root/miniconda3/envs/txy2/lib/python3.7/site-packages/tensorflow_core/python/ops/gen_nn_ops.py", line 5662, in <listcomp> ksize = [_execute.make_int(_i, "ksize") for _i in ksize] File "/root/miniconda3/envs/txy2/lib/python3.7/site-packages/tensorflow_core/python/eager/execute.py", line 169, in make_int (arg_name, repr(v))) TypeError: Expected int for argument 'ksize' not <tf.Tensor 'model_0/feature_fusion/SpatialPyramidPooling/strided_slice_2:0' shape=() dtype=int32>. Process finished with exit code 1

这个错误是由于传递给函数的参数 ksize 需要是整数类型，但是你传递了一个 Tensor 对象。你需要确保将 Tensor 转换为整数类型后再传递给函数。你可以使用 TensorFlow 的 `tf.cast()` 函数将 Tensor 转换为整数类型，例如：`tf.cast(ksize_tensor, tf.int32)`。你需要找到代码中使用了 `tf.nn.max_pool()` 函数的部分，并检查是否在调用该函数时传递了一个 Tensor 类型的 ksize 参数，如果是，则需要将其转换为整数类型。例如，你可以将以下代码：

pool = tf.nn.max_pool(input, ksize=ksize_tensor, strides=[1, strides[0], strides[1], 1], padding='VALID')

修改为：

ksize = tf.cast(ksize_tensor, tf.int32)
pool = tf.nn.max_pool(input, ksize=[1, ksize, ksize, 1], strides=[1, strides[0], strides[1], 1], padding='VALID')

这应该可以解决你的问题。