op_analysis_round2.py

import openai
from openai import OpenAI
import json


openai.api_key = "USE_YOUR_API"  
# Replace with your actual API key


def analyze_code_with_chatgpt(all_code):
    client = OpenAI(api_key=openai.api_key)
    response = client.chat.completions.create(
    model="gpt-4o-2024-05-13", # can be "gpt-4o-2024-05-13" or "gpt-4o-2024-08-06"
    messages=[
    {
      "role": "system",
      "content": [
        {
          "type": "text",
          "text": "<Task>\nAs a TensorFlow code expert and security expert, I want you to analyze the capabilities of the given TensorFlow API except for mathematical calculation, including high-level Python API and low-level C++ code of op implementation, using your understanding of the TensorFlow source code. \n</Task>\n\n<Description>\nI want you to think step by step.\nStep 1: Analyze the Python code comments to see if it says that the API can perform file access (file read, file write, directory read, directory write), network access (network send, network receive), and process management(process create, abort, sleep), pure mathematical calculation. For example, the string \"Args:\\n server_address: A 'Tensor' of type 'string'.\" in the comment indicates that the API has a \"server_address\" parameter and may have network access capabilities.\nStep 2: Analyze macros (like OP_REQUIRES) in C++ code, they are responsible for reading input and attributes from cross-language context (ctx or context) and processing them. I want you to determine whether the function and parameter names of these macros are obvious file access, network access, pure mathematical calculation, etc. For example, there is a \"OP_REQUIRES_OK(ctx,AppendStringToFile(file_path_, ended_msg, ctx->env()));\" macro. The \"AppendStringToFile\" function and the \"file_path_\" parameter in it indicate that the API can have the file access capability.\nStep 3: Analyze the function implementation logic, and determine whether it has file access, network access, process management, pure mathematical calculation, etc. capabilities. For example, the invoking of \"const auto status =  env->NewReadOnlyMemoryRegionFromFile(name, &memory_region_);\" indicates that the API has the file access capability.\nStep 4: By analyzing the results of the above three small steps, give me the final conclusion, which capabilities the API has.\n</Description>\n\n<Capability classification>\n1.file access(file read, file write, directory read, directory write)\n2.network access(network send, network receive)\n3.process management(process create, abort, sleep)\n4.pure calculation(mathematical calculation, internal data management, encoding & decoding)\n5.code execution(user-controlled function execution)\n</Capability classification>\n\n<Input>\nI will give you a JSON format like this:\n{\nPython_codes: <Python codes>,\nCpp_codes: <C++ codes>,\n}\n</Input>\n\n<Output>\nI want you to output a JSON format like this:\n{\nstep1_result: one or more of file access(file read, file write, directory read, directory write), network access (network send, network receive), process management(process create, abort, sleep), pure calculation(mathematical calculation, internal data management, encoding & decoding) and code execution(user-controlled function execution). \nstep2_result:one or more of file access(file read, file write, directory read, directory write), network access (network send, network receive), process management(process create, abort, sleep), pure calculation(mathematical calculation, internal data management, encoding & decoding) and code execution(user-controlled function execution). \nstep3_result: one or more of file access(file read, file write, directory read, directory write), network access (network send, network receive), process management(process create, abort, sleep), pure calculation(mathematical calculation, internal data management, encoding & decoding) and code execution(user-controlled function execution). \nfinal_capability: one or more of file access(file read, file write, directory read, directory write), network access (network send, network receive), process management(process create, abort, sleep), pure calculation(mathematical calculation, internal data management, encoding & decoding) and code execution(user-controlled function execution). \n},\nIf the API is not equipped with this capability, it will be marked as \"no\". If you can't judge, the result is only \"unsure\".  If the final_result is \"no\", you should classify it into a new capability.\n</Output>"
        }
      ]
    },
    {
      "role": "user",
      "content": [
        {
          "text": " {\n\"python_code\": \"def py_func(input, token: str, Tout, name=None):\\n  r\\\"\\\"\\\"Invokes a python function to compute func(input)->output.\\n\\n  This operation is considered stateful. For a stateless version, see\\n  PyFuncStateless.\\n\\n  Args:\\n    input: A list of `Tensor` objects.\\n      List of Tensors that will provide input to the Op.\\n    token: A `string`.\\n      A token representing a registered python function in this address space.\\n    Tout: A list of `tf.DTypes`. Data types of the outputs from the op.\\n      The length of the list specifies the number of outputs.\\n    name: A name for the operation (optional).\\n\\n  Returns:\\n    A list of `Tensor` objects of type `Tout`.\\n  \\\"\\\"\\\"\\n  _ctx = _context._context or _context.context()\\n  tld = _ctx._thread_local_data\\n  if tld.is_eager:\\n    try:\\n      _result = pywrap_tfe.TFE_Py_FastPathExecute(\\n        _ctx, \\\"PyFunc\\\", name, input, \\\"token\\\", token, \\\"Tout\\\", Tout)\\n      return _result\\n    except _core._NotOkStatusException as e:\\n      _ops.raise_from_not_ok_status(e, name)\\n    except _core._FallbackException:\\n      pass\\n    try:\\n      return py_func_eager_fallback(\\n          input, token=token, Tout=Tout, name=name, ctx=_ctx)\\n    except _core._SymbolicException:\\n      pass  # Add nodes to the TensorFlow graph.\\n  # Add nodes to the TensorFlow graph.\\n  token = _execute.make_str(token, \\\"token\\\")\\n  if not isinstance(Tout, (list, tuple)):\\n    raise TypeError(\\n        \\\"Expected list for 'Tout' argument to \\\"\\n        \\\"'py_func' Op, not %r.\\\" % Tout)\\n  Tout = [_execute.make_type(_t, \\\"Tout\\\") for _t in Tout]\\n  _, _, _op, _outputs = _op_def_library._apply_op_helper(\\n        \\\"PyFunc\\\", input=input, token=token, Tout=Tout, name=name)\\n  _result = _outputs[:]\\n  if not _result:\\n    return _op\\n  if _execute.must_record_gradient():\\n    _attrs = (\\\"token\\\", _op.get_attr(\\\"token\\\"), \\\"Tin\\\", _op.get_attr(\\\"Tin\\\"),\\n              \\\"Tout\\\", _op.get_attr(\\\"Tout\\\"))\\n    _inputs_flat = _op.inputs\\n    _execute.record_gradient(\\n        \\\"PyFunc\\\", _inputs_flat, _attrs, _result)\\n  return _result\",\n    \"cpp_code\": \"  void Compute(OpKernelContext* ctx) override {\\n    PyCall call;\\n    call.token = token_;\\n    call.eager = eager_;\\n    if (call.eager) {\\n      call.device = dynamic_cast<Device*>(ctx->device());\\n      if (call.device == nullptr) {\\n        ctx->CtxFailureWithWarning(errors::Internal(\\n            \\\"Unrecognized device class: \\\", ctx->device()->name()));\\n        return;\\n      }\\n      call.eager_async = eager_async_;\\n    }\\n\\n    VLOG(1) << \\\"PyFuncOp of token \\\" << call.token << \\\"is called.\\\";\\n\\n    for (int i = 0; i < ctx->num_inputs(); ++i) {\\n      call.ins.push_back(ctx->input(i));\\n    }\\n\\n    OP_REQUIRES(ctx, Py_IsInitialized(),\\n                errors::FailedPrecondition(\\n                    \\\"Python interpreter state is not initialized. \\\"\\n                    \\\"The process may be terminated.\\\"));\\n\\n    PyGILState_STATE py_threadstate;\\n    py_threadstate = PyGILState_Ensure();\\n    bool log_on_error;\\n    Status s = DoCallPyFunc(&call, &log_on_error);\\n    ClearDecrefCache();\\n    PyGILState_Release(py_threadstate);\\n\\n    if (!s.ok()) {\\n      if (log_on_error) {\\n        ctx->CtxFailureWithWarning(s);\\n      } else {\\n        ctx->CtxFailure(s);\\n      }\\n      return;\\n    }\\n\\n    OP_REQUIRES(ctx, static_cast<int32>(call.out.size()) == ctx->num_outputs(),\\n                errors::InvalidArgument(token_, \\\" returns \\\", call.out.size(),\\n                                        \\\" values, but expects to see \\\",\\n                                        ctx->num_outputs(), \\\" values.\\\"));\\n    for (size_t i = 0; i < call.out.size(); ++i) {\\n      const auto& t = call.out[i];\\n      OP_REQUIRES(\\n          ctx, t.dtype() == output_type(i),\\n          errors::InvalidArgument(i, \\\"-th value returned by \\\", token_, \\\" is \\\",\\n                                  DataTypeString(t.dtype()), \\\", but expects \\\",\\n                                  DataTypeString(output_type(i))));\\n      ctx->set_output(i, t);\\n    }\\n  }\\n\",\n}",
          "type": "text"
        }
      ]
    },
    {
      "role": "assistant",
      "content": [
        {
          "type": "text",
          "text": "```json\n{\n  \"step1_result\": {\"file access\": \"no\", \"network access\": \"no\", \"process management\": \"no\", \"pure calculation\": \"no\", \"code execution\": \"user-controlled function execution\"},\n  \"step2_result\": {\"file access\": \"no\", \"network access\": \"no\", \"process management\": \"no\", \"pure calculation\": \"no\", \"code execution\": \"user-controlled function execution\"},\n  \"step3_result\": {\"file access\": \"no\", \"network access\": \"no\", \"process management\": \"no\", \"pure calculation\": \"no\", \"code execution\": \"user-controlled function execution\"},\n  \"final_capability\": [\"code execution (user-controlled function execution)\"]\n}\n```"
        }
      ]
    },
    {
      "role": "user",
      "content": [
        {
          "text": "{\n \"python_code\": \"def immutable_const(dtype: TV_ImmutableConst_dtype, shape, memory_region_name: str, name=None) -> Annotated[Any, TV_ImmutableConst_dtype]:\\n  r\\\"\\\"\\\"Returns immutable tensor from memory region.\\n\\n  The current implementation memmaps the tensor from a file.\\n\\n  Args:\\n    dtype: A `tf.DType`. Type of the returned tensor.\\n    shape: A `tf.TensorShape` or list of `ints`. Shape of the returned tensor.\\n    memory_region_name: A `string`.\\n      Name of readonly memory region used by the tensor, see\\n      NewReadOnlyMemoryRegionFromFile in tensorflow::Env.\\n    name: A name for the operation (optional).\\n\\n  Returns:\\n    A `Tensor` of type `dtype`.\\n  \\\"\\\"\\\"\\n  _ctx = _context._context or _context.context()\\n  tld = _ctx._thread_local_data\\n  if tld.is_eager:\\n    try:\\n      _result = pywrap_tfe.TFE_Py_FastPathExecute(\\n        _ctx, \\\"ImmutableConst\\\", name, \\\"dtype\\\", dtype, \\\"shape\\\", shape,\\n        \\\"memory_region_name\\\", memory_region_name)\\n      return _result\\n    except _core._NotOkStatusException as e:\\n      _ops.raise_from_not_ok_status(e, name)\\n    except _core._FallbackException:\\n      pass\\n    try:\\n      return immutable_const_eager_fallback(\\n          dtype=dtype, shape=shape, memory_region_name=memory_region_name,\\n          name=name, ctx=_ctx)\\n    except _core._SymbolicException:\\n      pass  # Add nodes to the TensorFlow graph.\\n  # Add nodes to the TensorFlow graph.\\n  dtype = _execute.make_type(dtype, \\\"dtype\\\")\\n  shape = _execute.make_shape(shape, \\\"shape\\\")\\n  memory_region_name = _execute.make_str(memory_region_name, \\\"memory_region_name\\\")\\n  _, _, _op, _outputs = _op_def_library._apply_op_helper(\\n        \\\"ImmutableConst\\\", dtype=dtype, shape=shape,\\n                          memory_region_name=memory_region_name, name=name)\\n  _result = _outputs[:]\\n  if _execute.must_record_gradient():\\n    _attrs = (\\\"dtype\\\", _op._get_attr_type(\\\"dtype\\\"), \\\"shape\\\",\\n              _op.get_attr(\\\"shape\\\"), \\\"memory_region_name\\\",\\n              _op.get_attr(\\\"memory_region_name\\\"))\\n    _inputs_flat = _op.inputs\\n    _execute.record_gradient(\\n        \\\"ImmutableConst\\\", _inputs_flat, _attrs, _result)\\n  _result, = _result\\n  return _result\",\n    \"cpp_code\": \"void ImmutableConstantOp::Compute(OpKernelContext* ctx) {\\n  std::unique_ptr<MemmappedTensorAllocator> allocator(\\n      new MemmappedTensorAllocator());\\n\\n  OP_REQUIRES_OK(ctx,\\n                 allocator->InitializeFromRegion(region_name_, ctx->env()));\\n  OP_REQUIRES(ctx, dtype_ != DT_STRING,\\n              errors::Unimplemented(\\\"Sorry, DT_STRING is not currently \\\"\\n                                    \\\"supported for ImmutableConstOp.\\\"));\\n  ctx->set_output(0, Tensor(allocator.get(), dtype_, shape_));\\n  OP_REQUIRES_OK(ctx, allocator->allocation_status());\\n  allocator.release()->set_delete_on_deallocate();\\n}\\n\",\n}",
          "type": "text"
        }
      ]
    },
    {
      "role": "assistant",
      "content": [
        {
          "type": "text",
          "text": "```json\n{\n  \"step1_result\": {\"file access\": \"file read\", \"network access\": \"no\", \"process management\": \"no\", \"pure calculation\": \"no\", \"code execution\": \"no\"},\n  \"step2_result\": {\"file access\": \"file read\", \"network access\": \"no\", \"process management\": \"no\", \"pure calculation\": \"no\", \"code execution\": \"no\"},\n  \"step3_result\": {\"file access\": \"file read\", \"network access\": \"no\", \"process management\": \"no\", \"pure calculation\": \"no\", \"code execution\": \"no\"},\n  \"final_capability\": [\"file access (file read)\"]\n}\n```"
        }
      ]
    },
    {
      "role": "user",
      "content": [
        {
          "type": "text",
          "text": "{\nPython_codes: '@_dispatch.add_fallback_dispatch_list\\n@_dispatch.add_type_based_api_dispatcher\\n@tf_export(\\'io.decode_base64\\', v1=[\\'io.decode_base64\\', \\'decode_base64\\'])\\n@deprecated_endpoints(\\'decode_base64\\')\\ndef decode_base64(input: Annotated[Any, _atypes.String], name=None) -> Annotated[Any, _atypes.String]:\\n    \"\"\"Decode web-safe base64-encoded strings.\\n\\n  Input may or may not have padding at the end. See\\n  [EncodeBase64](https://www.tensorflow.org/api_docs/python/tf/io/encode_base64)\\n  for padding. Web-safe means that input must use - and _ instead of + and /.\\n\\n  Args:\\n    input: A `Tensor` of type `string`. Base64 strings to decode.\\n    name: A name for the operation (optional).\\n\\n  Returns:\\n    A `Tensor` of type `string`.\\n  \"\"\"\\n    _ctx = _context._context or _context.context()\\n    tld = _ctx._thread_local_data\\n    if tld.is_eager:\\n        try:\\n            _result = pywrap_tfe.TFE_Py_FastPathExecute(_ctx, \\'DecodeBase64\\', name, input)\\n            return _result\\n        except _core._NotOkStatusException as e:\\n            _ops.raise_from_not_ok_status(e, name)\\n        except _core._FallbackException:\\n            pass\\n        try:\\n            _result = _dispatcher_for_decode_base64((input, name), None)\\n            if _result is not NotImplemented:\\n                return _result\\n            return decode_base64_eager_fallback(input, name=name, ctx=_ctx)\\n        except _core._SymbolicException:\\n            pass\\n        except (TypeError, ValueError):\\n            _result = _dispatch.dispatch(decode_base64, (), dict(input=input, name=name))\\n            if _result is not _dispatch.OpDispatcher.NOT_SUPPORTED:\\n                return _result\\n            raise\\n    else:\\n        _result = _dispatcher_for_decode_base64((input, name), None)\\n        if _result is not NotImplemented:\\n            return _result\\n    try:\\n        (_, _, _op, _outputs) = _op_def_library._apply_op_helper(\\'DecodeBase64\\', input=input, name=name)\\n    except (TypeError, ValueError):\\n        _result = _dispatch.dispatch(decode_base64, (), dict(input=input, name=name))\\n        if _result is not _dispatch.OpDispatcher.NOT_SUPPORTED:\\n            return _result\\n        raise\\n    _result = _outputs[:]\\n    if _execute.must_record_gradient():\\n        _attrs = ()\\n        _inputs_flat = _op.inputs\\n        _execute.record_gradient(\\'DecodeBase64\\', _inputs_flat, _attrs, _result)\\n    (_result,) = _result\\n    return _result' ,\nCpp_codes: '  void Compute(OpKernelContext* context) override {\\n    const Tensor& input_tensor = context->input(0);\\n    Tensor* output_tensor = nullptr;\\n    OP_REQUIRES_OK(context, context->allocate_output(0, input_tensor.shape(),\\n                                                     &output_tensor));\\n\\n    auto input = input_tensor.flat<tstring>();\\n    auto output = output_tensor->flat<tstring>();\\n\\n    for (int64_t i = 0; i < input.dimension(0); ++i) {\\n      OP_REQUIRES_OK(context, Base64Decode(input(i), &output(i)));\\n    }\\n  }\\n') ',\n}"
        }
      ]
    },
    {
      "role": "assistant",
      "content": [
        {
          "type": "text",
          "text": "```json\n{\n  \"step1_result\": {\"file access\": \"no\", \"network access\": \"no\", \"process management\": \"no\", \"pure calculation\": \"encode and decode\", \"code execution\": \"no\"},\n  \"step2_result\": {\"file access\": \"no\", \"network access\": \"no\", \"process management\": \"no\", \"pure calculation\": \"encode and decode\", \"code execution\": \"no\"},\n  \"step3_result\": {\"file access\": \"no\", \"network access\": \"no\", \"process management\": \"no\", \"pure calculation\": \"encode and decode\", \"code execution\": \"no\"},\n  \"final_capability\": [\"pure calculation (encode and decode)\"]\n}\n```"
        }
      ]
    },
    {
      "role": "user",
      "content": [
        {
          "type": "text",
          "text": "{Python_code: 'def pad(input: Annotated[Any, TV_Pad_T], paddings: Annotated[Any, TV_Pad_Tpaddings], name=None) -> Annotated[Any, TV_Pad_T]: r\"\"\"Pads a tensor with zeros. This operation pads a `input` with zeros according to the `paddings` you specify. `paddings` is an integer tensor with shape `[Dn, 2]`, where n is the rank of `input`. For each dimension D of `input`, `paddings[D, 0]` indicates how many zeros to add before the contents of `input` in that dimension, and `paddings[D, 1]` indicates how many zeros to add after the contents of `input` in that dimension. The padded size of each dimension D of the output is: `paddings(D, 0) + input.dim_size(D) + paddings(D, 1)` For example: ``` # 't' is [[1, 1], [2, 2]] # 'paddings' is [[1, 1], [2, 2]] # rank of 't' is 2 pad(t, paddings) ==> [[0, 0, 0, 0, 0, 0] [0, 0, 1, 1, 0, 0] [0, 0, 2, 2, 0, 0] [0, 0, 0, 0, 0, 0]] ``` Args: input: A `Tensor`. paddings: A `Tensor`. Must be one of the following types: `int32`, `int64`. name: A name for the operation (optional). Returns: A `Tensor`. Has the same type as `input`. \"\"\" _ctx = _context._context or _context.context() tld = _ctx._thread_local_data if tld.is_eager: try: _result = pywrap_tfe.TFE_Py_FastPathExecute( _ctx, \"Pad\", name, input, paddings) return _result except _core._NotOkStatusException as e: _ops.raise_from_not_ok_status(e, name) except _core._FallbackException: pass try: return pad_eager_fallback( input, paddings, name=name, ctx=_ctx) except _core._SymbolicException: pass # Add nodes to the TensorFlow graph. # Add nodes to the TensorFlow graph. _, _, _op, _outputs = _op_def_library._apply_op_helper( \"Pad\", input=input, paddings=paddings, name=name) _result = _outputs[:] if _execute.must_record_gradient(): _attrs = (\"T\", _op._get_attr_type(\"T\"), \"Tpaddings\", _op._get_attr_type(\"Tpaddings\")) _inputs_flat = _op.inputs _execute.record_gradient( \"Pad\", _inputs_flat, _attrs, _result) _result, = _result return _result',\n\nCpp_codes: 'void Compile(XlaOpKernelContext* ctx) override { const TensorShape input_shape = ctx->InputShape(\"input\"); const TensorShape pad_shape = ctx->InputShape(\"paddings\"); const int dims = input_shape.dims(); OP_REQUIRES( ctx, TensorShapeUtils::IsMatrix(pad_shape) && pad_shape.dim_size(1) == 2, errors::InvalidArgument(\"paddings must be a matrix with 2 columns: \", pad_shape.DebugString())); OP_REQUIRES( ctx, dims == pad_shape.dim_size(0), errors::InvalidArgument( \"The first dimension of paddings must be the rank of inputs\", pad_shape.DebugString(), \" \", input_shape.DebugString())); xla::XlaOp input = ctx->Input(\"input\"); if (dims == 0) { ctx->SetOutput(0, input); return; } xla::Literal pad_literal; OP_REQUIRES_OK(ctx, ctx->ConstantInputAsInt64Literal( \"paddings\", &pad_literal, xla::ValueInferenceMode::kUpperBound)); xla::Literal padding_dynamism_literal; OP_REQUIRES_OK( ctx, ctx->ResolveInputDynamism(\"paddings\", &padding_dynamism_literal)); xla::PaddingConfig config; for (int i = 0; i < dims; ++i) { auto* dim = config.add_dimensions(); int before = pad_literal.Get<int64_t>({i, 0}); int after = pad_literal.Get<int64_t>({i, 1}); OP_REQUIRES(ctx, before >= 0 && after >= 0, errors::InvalidArgument( \"Paddings must be non-negative: \", before, \" \", after)); dim->set_edge_padding_low(before); dim->set_edge_padding_high(after); } xla::XlaOp constant_values; xla::XlaOp pad; if (ctx->num_inputs() == 3) { OP_REQUIRES( ctx, TensorShapeUtils::IsScalar(ctx->InputShape(\"constant_values\")), errors::InvalidArgument(\"constant_values must be a scalar.\")); pad = xla::Pad(input, ctx->Input(\"constant_values\"), config); } else { auto zero = XlaHelpers::Zero(ctx->builder(), input_type(0)); pad = xla::Pad(input, zero, config); } for (int i = 0; i < dims; ++i) { bool low_pad_is_dynamic = padding_dynamism_literal.Get<bool>({i, 0}); OP_REQUIRES( ctx, !low_pad_is_dynamic, errors::InvalidArgument(\"low_pad in Pad op has to be static.\")); bool high_pad_is_dynamic = padding_dynamism_literal.Get<bool>({i, 1}); if (high_pad_is_dynamic) { xla::XlaOp high_pad_size = xla::Slice(ctx->Input(\"paddings\"), {i, 1}, {i + 1, 2}, {1, 1}); high_pad_size = xla::Reshape(high_pad_size, {}); high_pad_size = xla::ConvertElementType(high_pad_size, xla::S32); xla::XlaOp low_pad_size = xla::ConstantR0<int32>( ctx->builder(), pad_literal.Get<int64_t>({i, 0})); xla::XlaOp input_size = xla::GetDimensionSize(input, i); xla::XlaOp total_size = low_pad_size + input_size + high_pad_size; auto size_upper_bound_status_or = ctx->value_inference().AnalyzeConstant( total_size, xla::ValueInferenceMode::kUpperBound); OP_REQUIRES_OK(ctx, size_upper_bound_status_or.status()); auto size_upper_bound = size_upper_bound_status_or.value().Get<int32>({}); OP_REQUIRES( ctx, size_upper_bound.has_value(), errors::InvalidArgument( \"Failed to infer upperbound of total size after padding.\")); pad = xla::SliceInDim(pad, 0, size_upper_bound.value(), 1, i); pad = xla::SetDimensionSize(pad, total_size, i); } } ctx->SetOutput(0, pad); }'\n}"
        }
      ]
    },
    {
      "role": "assistant",
      "content": [
        {
          "type": "text",
          "text": "```json\n{\n  \"step1_result\": {\"file access\": \"no\", \"network access\": \"no\", \"process management\": \"no\", \"pure calculation\": \"mathematical calculation\", \"code execution\": \"no\"},\n  \"step2_result\": {\"file access\": \"no\", \"network access\": \"no\", \"process management\": \"no\", \"pure calculation\": \"mathematical calculation\", \"code execution\": \"no\"},\n  \"step3_result\": {\"file access\": \"no\", \"network access\": \"no\", \"process management\": \"no\", \"pure calculation\": \"mathematical calculation\", \"code execution\": \"no\"},\n  \"final_capability\": [\"pure calculation (mathematical calculation)\"]\n}\n```"
        }
      ]
    },
    {
      "role": "user",
      "content": [
        {
          "type": "text",
          "text": "{\nPython_codes:'def debug_identity_v3(input: Annotated[Any, TV_DebugIdentityV3_T], device_name: str=\\'\\', tensor_name: str=\\'\\', io_of_node: str=\\'\\', is_input: bool=False, io_index: int=-1, debug_urls=[], gated_grpc: bool=False, name=None) -> Annotated[Any, TV_DebugIdentityV3_T]:\\n    \"\"\"Provides an identity mapping of the non-Ref type input tensor for debugging.\\n\\n  Provides an identity mapping of the non-Ref type input tensor for debugging.\\n\\n  Args:\\n    input: A `Tensor`. Input tensor, non-Reference type\\n    device_name: An optional `string`. Defaults to `\"\"`.\\n      Name of the device on which the tensor resides.\\n    tensor_name: An optional `string`. Defaults to `\"\"`.\\n      Name of the input tensor.\\n    io_of_node: An optional `string`. Defaults to `\"\"`.\\n      Name of the node of which the tensor is an input or output.\\n    is_input: An optional `bool`. Defaults to `False`.\\n      If true, the tensor is an input of the node; otherwise the output.\\n    io_index: An optional `int`. Defaults to `-1`.\\n      The index of which the tensor is an input or output of the node.\\n    debug_urls: An optional list of `strings`. Defaults to `[]`.\\n      List of URLs to debug targets, e.g.,\\n        file:///foo/tfdbg_dump, grpc:://localhost:11011\\n    gated_grpc: An optional `bool`. Defaults to `False`.\\n      Whether this op will be gated. If any of the debug_urls of this\\n        debug node is of the grpc:// scheme, when the value of this attribute is set\\n        to True, the data will not actually be sent via the grpc stream unless this\\n        debug op has been enabled at the debug_url. If all of the debug_urls of this\\n        debug node are of the grpc:// scheme and the debug op is enabled at none of\\n        them, the output will be an empty Tensor.\\n    name: A name for the operation (optional).\\n\\n  Returns:\\n    A `Tensor`. Has the same type as `input`.\\n  \"\"\"\\n    _ctx = _context._context or _context.context()\\n    tld = _ctx._thread_local_data\\n    if tld.is_eager:\\n        try:\\n            _result = pywrap_tfe.TFE_Py_FastPathExecute(_ctx, \\'DebugIdentityV3\\', name, input, \\'device_name\\', device_name, \\'tensor_name\\', tensor_name, \\'io_of_node\\', io_of_node, \\'is_input\\', is_input, \\'io_index\\', io_index, \\'debug_urls\\', debug_urls, \\'gated_grpc\\', gated_grpc)\\n            return _result\\n        except _core._NotOkStatusException as e:\\n            _ops.raise_from_not_ok_status(e, name)\\n        except _core._FallbackException:\\n            pass\\n        try:\\n            return debug_identity_v3_eager_fallback(input, device_name=device_name, tensor_name=tensor_name, io_of_node=io_of_node, is_input=is_input, io_index=io_index, debug_urls=debug_urls, gated_grpc=gated_grpc, name=name, ctx=_ctx)\\n        except _core._SymbolicException:\\n            pass\\n    if device_name is None:\\n        device_name = \\'\\'\\n    device_name = _execute.make_str(device_name, \\'device_name\\')\\n    if tensor_name is None:\\n        tensor_name = \\'\\'\\n    tensor_name = _execute.make_str(tensor_name, \\'tensor_name\\')\\n    if io_of_node is None:\\n        io_of_node = \\'\\'\\n    io_of_node = _execute.make_str(io_of_node, \\'io_of_node\\')\\n    if is_input is None:\\n        is_input = False\\n    is_input = _execute.make_bool(is_input, \\'is_input\\')\\n    if io_index is None:\\n        io_index = -1\\n    io_index = _execute.make_int(io_index, \\'io_index\\')\\n    if debug_urls is None:\\n        debug_urls = []\\n    if not isinstance(debug_urls, (list, tuple)):\\n        raise TypeError(\"Expected list for \\'debug_urls\\' argument to \\'debug_identity_v3\\' Op, not %r.\" % debug_urls)\\n    debug_urls = [_execute.make_str(_s, \\'debug_urls\\') for _s in debug_urls]\\n    if gated_grpc is None:\\n        gated_grpc = False\\n    gated_grpc = _execute.make_bool(gated_grpc, \\'gated_grpc\\')\\n    (_, _, _op, _outputs) = _op_def_library._apply_op_helper(\\'DebugIdentityV3\\', input=input, device_name=device_name, tensor_name=tensor_name, io_of_node=io_of_node, is_input=is_input, io_index=io_index, debug_urls=debug_urls, gated_grpc=gated_grpc, name=name)\\n    _result = _outputs[:]\\n    if _execute.must_record_gradient():\\n        _attrs = (\\'T\\', _op._get_attr_type(\\'T\\'), \\'device_name\\', _op.get_attr(\\'device_name\\'), \\'tensor_name\\', _op.get_attr(\\'tensor_name\\'), \\'io_of_node\\', _op.get_attr(\\'io_of_node\\'), \\'is_input\\', _op._get_attr_bool(\\'is_input\\'), \\'io_index\\', _op._get_attr_int(\\'io_index\\'), \\'debug_urls\\', _op.get_attr(\\'debug_urls\\'), \\'gated_grpc\\', _op._get_attr_bool(\\'gated_grpc\\'))\\n        _inputs_flat = _op.inputs\\n        _execute.record_gradient(\\'DebugIdentityV3\\', _inputs_flat, _attrs, _result)\\n    (_result,) = _result\\n    return _result',\nCpp_codes: '  void Compute(OpKernelContext* context) override {\\n    if (!ApplyGrpcGating(context)) {\\n      return;\\n    }\\n\\n    OP_REQUIRES_OK(context,\\n                   PublishTensor(context->input(0), context->step_id()));\\n    context->set_output(0, context->input(0));\\n  }\\n')\r\n',\n}"
        }
      ]
    },
    {
      "role": "assistant",
      "content": [
        {
          "type": "text",
          "text": "```json\n{\n  \"step1_result\": [\"file access\": \"file write\", \"network access\": \"network send, network receive\", \"process management\": \"no\", \"pure mathematical calculation\": \"no\"],\n  \"step2_result\": [\"file access\": \"no\", \"network access\": \"no\", \"process management\": \"no\", \"pure mathematical calculation\": \"no\"],\n  \"step3_result\": [\"file access (no)\", \"network access\": \"network send, network receive\", \"pure mathematical calculation\": \"no\", \"process management\": \"no\"],\n  \"final_capability\": [\"network access (network send, network receive)\"]\n}\n```"
        }
      ]
    },
    {
      "role": "user",
      "content": [
        {
          "type": "text",
          "text": "{}".format(all_code)
        }
      ]
    }
  ],
    temperature=1,
    max_tokens=256,
    top_p=1,
    frequency_penalty=0,
    presence_penalty=0
)

    output = response.choices[0].message.content
    return output