}
            break;
        case OPERA:
            if (isLocalFile) {
                url = url.replaceFirst("file:/+", systemProperties.getProperty("file.protocol.winlocal.opera", "file://"));
            } else {
                url = url.replaceFirst("file:/+", systemProperties.getProperty("file.protocol.opera", "file://"));
            }
            break;
        default:

    std::unordered_map<const Node*, std::vector<int>>* input_nodes)
    TF_EXCLUSIVE_LOCKS_REQUIRED(fn_body->mu) {
  input_tensors->reserve(ninputs);
  for (int i = 0; i < ninputs; ++i) {
    Node* node = inputs[i].oper ? &inputs[i].oper->node : nullptr;
    int idx = inputs[i].index;

    TF_RETURN_WITH_CONTEXT_IF_ERROR(
        fn_body->graph.IsValidOutputTensor(node, idx),

		return nil, errExpectedBool
	}
	return FromBool(!b), nil
}

func (e *ConditionOperand) evalNode(r Record, tableAlias string) (*Value, error) {
	opVal, opErr := e.Operand.evalNode(r, tableAlias)
	if opErr != nil || e.ConditionRHS == nil {
		return opVal, opErr
	}

	// Need to evaluate the ConditionRHS
	switch {
	case e.ConditionRHS.Compare != nil:

  TF_DeleteBuffer(buffer);
  TF_DeleteStatus(s);
  return ret;
}

bool GetNodeDef(TF_Operation* oper, tensorflow::NodeDef* node_def) {
  TF_Status* s = TF_NewStatus();
  TF_Buffer* buffer = TF_NewBuffer();
  TF_OperationToNodeDef(oper, buffer, s);
  bool ret = TF_GetCode(s) == TF_OK;
  EXPECT_EQ(TF_OK, TF_GetCode(s)) << TF_Message(s);

  TF_Output inputs[] = {{feed, 0}};
  TF_Output outputs[] = {{neg, 0}};
  *func = TF_GraphToFunction(func_graph.get(), name, append_hash, -1,
                             /*opers=*/nullptr, 1, inputs, 1, outputs,
                             /*output_names=*/nullptr,
                             /*opts=*/nullptr, description, s.get());
  ASSERT_EQ(TF_OK, TF_GetCode(s.get())) << TF_Message(s.get());

![ReDoc](https://fastapi.tiangolo.com/img/index/index-06-redoc-02.png)

### Nowoczesny Python

Wszystko opiera się na standardowych deklaracjach typu **Python 3.8** (dzięki Pydantic). Brak nowej składni do uczenia. Po prostu standardowy, współczesny Python.

    return absl::OkStatus();
  }

  tensorflow::FullTypeDef FullType() const override {
    const FullTypeDef* ft;
    mutex_lock l(graph_->mu);
    graph_->graph.NodeType(output_.oper->node.name(), &ft);
    if (ft == nullptr) {
      return FullTypeDef();
    } else {
      return *ft;
    }
  }

  TF_Output output_;

  // For LLVM style RTTI.

      TF_GraphToFunction(
          /* fn_body */ body.get(), /* fn_name */ function_name,
          /* append_hash_to_fn_name */ 0, /* num_opers */ 2,
          /* opers */ operations, /* ninputs */ 1, /* inputs */ &x,
          /* noutputs */ 1, /* outputs */ &y, /* output_names */ &output_name,
          /* opts */ nullptr, /* description */ "", /* status */ status),
      TF_DeleteFunction);

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