func (v *Value) InferBytesType() (err error) {
	b, ok := v.ToBytes()
	if !ok {
		return fmt.Errorf("InferByteType: Input is not bytes, but %v", v.GetTypeString())
	}

	// Check for numeric inference
	if x, ok := v.bytesToInt(); ok {
		v.setInt(x)
		return nil
	}
	if x, ok := v.bytesToFloat(); ok {
		v.setFloat(x)
		return nil
	}
	if x, ok := v.bytesToBool(); ok {
		v.setBool(x)

// `shape_inference::InferenceContext` constructor. Note the following:
//   - The inputs of the `op` are not used for shape inference. So, it is
//     OK to not have the inputs properly set in `op`. See `input_tensors`
//     if you want shape inference to consider the input tensors of the
//     op for shape inference.
//   - The types need not be set in `input_shapes` as it is not used.

   * use the {@code ArrayList} {@linkplain ArrayList#ArrayList() constructor} directly, taking
   * advantage of <a
   * href="https://docs.oracle.com/javase/tutorial/java/generics/genTypeInference.html#type-inference-instantiation">"diamond"
   * syntax</a>.
   */
  @GwtCompatible(serializable = true)
  @SuppressWarnings("NonApiType") // acts as a direct substitute for a constructor call

  }

  ~ShapeInferenceTest() override {
    TFE_DeleteContextOptions(tfe_context_options_);
    TFE_DeleteContext(tfe_context_);
    TF_DeleteStatus(status_);
  }

  // Checks the expected result of shape inference for the given `op`.
  void CheckOutputShapes(
      TFE_Op* op,
      const std::vector<absl::optional<std::vector<int64_t>>>& input_shapes_vec,
      const std::vector<TF_Tensor*>& input_tensors,

      status->status = TF_TensorToTensor(input_tensors[i], &input_tensor);
      if (!status->status.ok()) return;
      input_tensors_vector[i] = &input_tensor;
    }
  }

  // Create an inference context with dummy values, which will be updated later.
  InferenceContext c(TF_GRAPH_DEF_VERSION, node_def, op_reg_data->op_def,
                     std::vector<ShapeHandle>(num_inputs), input_tensors_vector,

  }

  TF_Output output_;

  // For LLVM style RTTI.
  static bool classof(const AbstractTensorHandle* ptr) {
    return ptr->getKind() == kGraph;
  }

 private:
  TF_Graph* graph_;  // For shape inference.
};

// GraphOperation wraps and populates a TF_OperationDescription.
class GraphOperation : public TracingOperation {
 public:
  explicit GraphOperation(TF_Graph* g) : TracingOperation(kGraph), g_(g) {}

        }
      }
      entries[entryIndex] = effectiveEntry;
    }
    if (duplicates != null) {
      // Explicit type parameters needed here to avoid a problem with nullness inference.
      entries = RegularImmutableMap.<K, V>removeDuplicates(entries, n, n - dupCount, duplicates);
      int newTableSize = Hashing.closedTableSize(entries.length, MAX_LOAD_FACTOR);
      if (newTableSize != tableSize) {

<span style="color: green;">INFO</span>:     Waiting for application startup.
<span style="color: green;">INFO</span>:     Application startup complete.
```

</div>

/// note

La commande `uvicorn main:app` fait référence à :

* `main` : le fichier `main.py` (le module Python).
* `app` : l'objet créé dans `main.py` via la ligne `app = FastAPI()`.

          <version>1.0.0+</version>
          <type>String</type>
          <description>Reference the invocation phase of the Mojo.</description>
        </field>
        <field>
          <name>executeGoal</name>
          <version>1.0.0+</version>
          <type>String</type>
          <description>Reference the invocation goal of the Mojo.</description>
        </field>
        <field>

		References: []Reference{
			{"Refer", "User", "UserReferID", "user_profiles", "", true},
			{"UserRefer", "Profile", "ProfileRefer", "user_profiles", "", false},
		},
	}, Relation{
		Name: "Profiles2", Type: schema.Many2Many, Schema: "User", FieldSchema: "Profile",
		JoinTable: JoinTable{Name: "user_profiles2", Table: "user_profiles2"},
		References: []Reference{