}
            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),

const char* TF_OperationName(TF_Operation* oper) {
  return oper->node.name().c_str();
}

const char* TF_OperationOpType(TF_Operation* oper) {
  return oper->node.type_string().c_str();
}

const char* TF_OperationDevice(TF_Operation* oper) {
  return oper->node.requested_device().c_str();
}

int TF_OperationNumOutputs(TF_Operation* oper) {
  return oper->node.num_outputs();
}

  bool found_neg = false;
  size_t pos = 0;
  TF_Operation* oper;
  while ((oper = TF_GraphNextOperation(graph, &pos)) != nullptr) {
    if (oper == feed) {
      EXPECT_FALSE(found_placeholder);
      found_placeholder = true;
    } else if (oper == three) {
      EXPECT_FALSE(found_scalar_const);
      found_scalar_const = true;
    } else if (oper == add) {
      EXPECT_FALSE(found_add);
      found_add = true;

		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:

///

## Zaawansowany poradnik

Jest też **Zaawansowany poradnik**, który możesz przeczytać po lekturze tego **Samouczka**.

**Zaawansowany poradnik** opiera się na tym samouczku, używa tych samych pojęć, żeby pokazać Ci kilka dodatkowych funkcji.

Najpierw jednak  powinieneś przeczytać **Samouczek** (czytasz go teraz).

![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.

units instead of `em`.\np {\n  margin-top: 0;\n  margin-bottom: $paragraph-margin-bottom;\n}\n\n// Abbreviations\n//\n// 1. Duplicate behavior to the data-* attribute for our tooltip plugin\n// 2. Add the correct text decoration in Chrome, Edge, IE, Opera, and Safari.\n// 3. Add explicit cursor to indicate changed behavior.\n// 4. Remove the bottom border in Firefox 39-.\n// 5. Prevent the text-decoration to be skipped.\n\nabbr[title],\nabbr[data-original-title] { // 1\n  text-decoration: underline;...

      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);