GraphDef gdef;
  GetGraphDef(host_graph_, &gdef);
  std::vector<std::pair<string, string>> grads = GetGradDefs(gdef);
  ASSERT_EQ(2, grads.size());
  ASSERT_EQ("FooFunc1", grads[0].first);
  ASSERT_EQ("MyGrad", grads[0].second);
  ASSERT_EQ("FooFunc2", grads[1].first);
  ASSERT_EQ("MyGrad", grads[1].second);

  TF_DeleteFunction(func1);
  TF_DeleteFunction(func2);
  TF_DeleteFunction(grad_func);

  if (TF_GetCode(status) != TF_OK) return;
  if (!grad) return;

  status->status = g->graph.AddFunctionDef(grad->record->fdef(),
                                           grad->record->stack_traces());
  if (TF_GetCode(status) != TF_OK) return;

  tensorflow::GradientDef gdef;
  gdef.set_function_name(func->record->fdef().signature().name());
  gdef.set_gradient_func(grad->record->fdef().signature().name());

// Adds a copy of function `func` and optionally its gradient function `grad`
// to `g`. Once `func`/`grad` is added to `g`, it can be called by creating
// an operation using the function's name.
// Any changes to `func`/`grad` (including deleting it) done after this method
// returns, won't affect the copy of `func`/`grad` in `g`.
// If `func` or `grad` are already in `g`, TF_GraphCopyFunction has no

[**sandbox**](https://developers.google.com/code-sandboxing). Memory corruptions
in TensorFlow ops can be recognized as security issues only if they are
reachable and exploitable through production-grade, benign models.

### Compilation

Compiling models via the recommended entry points described in
[XLA](https://www.tensorflow.org/xla) and
[JAX](https://jax.readthedocs.io/en/latest/jax-101/02-jitting.html)

# SMB3 Advanced Features Implementation Plan

## Overview
This document outlines the implementation plan for advanced SMB3 features that are currently not implemented in JCIFS. These features are essential for enterprise-grade SMB3 support, providing enhanced performance, reliability, and scalability.

## Implementation Phases

### Phase 1: SMB3 Lease Implementation (Foundation)
**Priority: HIGH** | **Estimated Effort: 3-4 weeks**

```Python
        ... "item_name": item.name ...
```

...a:

```Python
        ... "item_price": item.price ...
```

...e osserva come il tuo editor di testo autocompleterà gli attributi e sarà in grado di riconoscere i loro tipi:

![editor support](https://fastapi.tiangolo.com/img/vscode-completion.png)

  TF_DeleteBuffer(t_buffer);
}

REGISTER_OP("TestOpWithNoGradient")
    .Input("x: T")
    .Output("y: T")
    .Attr("T: {float, double}")
    .Doc(R"doc(
Test op with no grad registered.

x: input
y: output
)doc")
    .SetShapeFn(tensorflow::shape_inference::UnknownShape);

class CApiGradientsTest : public ::testing::Test {
 protected:
  CApiGradientsTest()

,KAAM,kCAEVZ,SAAU,CACNC,QAAS,eACTK,SAAU,IACVJ,QAAS,eACTC,SAAU,eACVC,QAAS,sBACTC,SAAU,WAEN,OAAQlV,KAAKqT,OACT,KAAK,EACL,KAAK,EACD,MAAO,0BACX,QACI,MAAO,4BAIvBsC,aAAc,CACVC,OAhGR,SAA2BkE,GAEvB,OAAI+lB,GADS/lB,EAAO1R,OAAO,EAAG0R,EAAO/L,QAAQ,OAElC,KAAO+L,EAEX,MAAQA,GA4FXjE,KA1FR,SAAyBiE,GAErB,OAAI+lB,GADS/lB,EAAO1R,OAAO,EAAG0R,EAAO/L,QAAQ,OAElC,QAAU+L,EAEd,SAAWA,GAsFdpM,EAAG,kBACHoI,GAAI,cACJpT,EAAGk9B,GACH7pB,GAAI,cACJC,EAAG4pB,GACH3pB,GAAI,aACJxD,EAAGmtB,GACH1pB,GAAI,UACJG,EAAGupB,GACHtpB,GAAI,cACJ...

*/\n}\n",":root,\n[data-bs-theme=\"light\"] {\n  // Note: Custom variable values only support SassScript inside `#{}`.\n\n  // Colors\n  //\n  // Generate palettes for full colors, grays, and theme colors.\n\n  @each $color, $value in $colors {\n    --#{$prefix}#{$color}: #{$value};\n  }\n\n  @each $color, $value in $grays {\n    --#{$prefix}gray-#{$color}: #{$value};\n  }\n\n  @each $color, $value in $theme-colors {\n    --#{$prefix}#{$color}: #{$value};\n  }\n\n  @each $color, $value in $theme-colors-rgb...

2F8A          ; mapped                 ; 8272          # 3.0  KANGXI RADICAL COLOR
2F8B          ; mapped                 ; 8278          # 3.0  KANGXI RADICAL GRASS
2F8C          ; mapped                 ; 864D          # 3.0  KANGXI RADICAL TIGER
2F8D          ; mapped                 ; 866B          # 3.0  KANGXI RADICAL INSECT