# Sub-dependencies { #sub-dependencies }

You can create dependencies that have **sub-dependencies**.

They can be as **deep** as you need them to be.

**FastAPI** will take care of solving them.

## First dependency "dependable" { #first-dependency-dependable }

You could create a first dependency ("dependable") like:

{* ../../docs_src/dependencies/tutorial005_an_py310.py hl[8:9] *}

        assertFalse(a.equals("not a blob"), "equals(other type) should be false");
    }

    // Ensures clone() returns a deep copy; mutations are independent across instances
    @Test
    @DisplayName("clone: returns deep copy and independent state")
    void clone_returnsDeepCopy() {
        // Arrange
        byte[] data = new byte[] { 10, 20, 30 };

        }
      }
    }
  ]
}
```

### v1.3+ versions

Version 1.3 introduces changes to help with [faster metadata reads and updates](https://blog.min.io/minio-versioning-metadata-deep-dive/)

| Entry           | Encoding                    | Content
| ----------------|-----------------------------|----------------------------------------

            call.executeAsync()
          }
        }
      }

      assertThat(call.canceled).isTrue()
      assertThat(call.responseClosed).isTrue()
    }
  }

  /** A call that keeps track of whether its response body is closed. */
  private class ClosableCall : FailingCall() {
    private val response =
      Response
        .Builder()
        .request(Request("https://example.com/".toHttpUrl()))

designs the management of shared state is built into the system, TensorFlow enables developers to experiment with novel optimizations and training algorithms. TensorFlow supports a variety of applications, with a focus on training and inference on deep neural networks. Several Google services use TensorFlow in production, we have released it as an open-source project, and it has become widely used for machine learning research. In this paper, we describe the TensorFlow dataflow model and demonstrate...

{* ../../docs_src/path_params/tutorial005_py310.py hl[1,6:9] *}

/// tip | Astuce

Si vous vous demandez, « AlexNet », « ResNet » et « LeNet » sont juste des noms de <dfn title="Techniquement, architectures de modèles de Deep Learning">modèles</dfn> de Machine Learning.

///

### Déclarer un paramètre de chemin { #declare-a-path-parameter }

          sb.append(DELIMITER_STRING);
        }
        sb.append(comp);
      }
      dummy ^= sb.toString().length();
    }
    return dummy;
  }

  /**
   * Similar to the above, but keeps a boolean flag rather than checking for the string accumulated
   * so far being empty. As a result, it does not have the above-mentioned bug.
   */
  @Benchmark
  int booleanIfFirst(int reps) {
    int dummy = 0;

          sb.append(DELIMITER_STRING);
        }
        sb.append(comp);
      }
      dummy ^= sb.toString().length();
    }
    return dummy;
  }

  /**
   * Similar to the above, but keeps a boolean flag rather than checking for the string accumulated
   * so far being empty. As a result, it does not have the above-mentioned bug.
   */
  @Benchmark
  int booleanIfFirst(int reps) {
    int dummy = 0;

   * IllegalArgumentException} if passed a {@code fromKey} less than an earlier {@code fromKey}.
   * However, this method doesn't throw an exception in that situation, but instead keeps the
   * original {@code fromKey}. Similarly, this method keeps the original {@code toKey}, instead of
   * throwing an exception, if passed a {@code toKey} greater than an earlier {@code toKey}.
   */
  @Override

  }

  @ListFeature.Require(absent = SUPPORTS_ADD_WITH_INDEX)
  @CollectionSize.Require(absent = ZERO)
  /*
   * absent = ZERO isn't required, since unmodList.add() must
   * throw regardless, but it keeps the method name accurate.
   */
  public void testAddAtIndex_unsupportedPresent() {
    assertThrows(UnsupportedOperationException.class, () -> getList().add(0, e0()));
    expectUnchanged();
  }