return fmt.Errorf("Expected command line argument %s, seen %s", cmdLine,
				s2.CmdLines[i])
		}
	}

	if reflect.DeepEqual(s1.MinioEnv, s2.MinioEnv) {
		return nil
	}

	// Report differences in environment variables.
	var missing []string
	var mismatching []string
	for k, v := range s1.MinioEnv {
		ev, ok := s2.MinioEnv[k]
		if !ok {
			missing = append(missing, k)
		} else if v != ev {

The MinIO Security Token Service (STS) is an endpoint service that enables clients to request temporary credentials for MinIO resources. Temporary credentials work almost identically to default admin credentials, with some differences:

All the data conversion, validation, documentation, etc. will still work as normally.

That way, we can declare just the differences between the models (with plaintext `password`, with `hashed_password` and without password):

//// tab | Python 3.10+

```Python hl_lines="7  13-14  17-18  21-22"
{!> ../../docs_src/extra_models/tutorial002_py310.py!}

      )
      .build()
  }

  private fun buildServerSslSocketFactory(): SSLSocketFactory {
    // The test uses JDK default SSL Context instead of the Platform provided one
    // as Conscrypt seems to have some differences, we only want to test client side here.
    return try {
      val keyManager =
        newKeyManager(
          null,
          serverCert,
          serverIntermediateCa.certificate,
        )

```Python hl_lines="16-18  21-22  28-32"
{!../../docs_src/openapi_callbacks/tutorial001.py!}
```

There are 2 main differences from a normal *path operation*:

* It doesn't need to have any actual code, because your app will never call this code. It's only used to document the *external API*. So, the function could just have `pass`.

Eager mode lets users write imperative-style statements that can be easily
inspected and debugged and it is intended to be used during the development
phase.

As part of the differences that make Eager mode easier to debug, the [shape
inference
functions](https://www.tensorflow.org/guide/create_op#define_the_op_interface)
are skipped, and any checks implemented inside the shape inference code are not

   *
   * <p>This fit minimizes the root-mean-square error in {@code y} as a function of {@code x}. This
   * error is defined as the square root of the mean of the squares of the differences between the
   * actual {@code y} values of the data and the values predicted by the fit for the {@code x}
   * values (i.e. it is the square root of the mean of the squares of the vertical distances between

   *
   * <p>This fit minimizes the root-mean-square error in {@code y} as a function of {@code x}. This
   * error is defined as the square root of the mean of the squares of the differences between the
   * actual {@code y} values of the data and the values predicted by the fit for the {@code x}
   * values (i.e. it is the square root of the mean of the squares of the vertical distances between

//
// Assumes that the provided 'ctx' outlives the returned TFE_Op, i.e.,
// TFE_DeleteOp() is called before TFE_DeleteContext().
//
// Very similar to TF_OperationDescription with some differences:
// (1) TF_Output or TFE_TensorHandle* as arguments to TF_AddInput,
//     TF_AddInputList
// (2) TF_ColocateWith, TF_AddControlInput etc. do not make sense.

 * Verify.verify(bill.status() == Status.UNPAID,
 *     "Unexpected bill status: %s", bill.status());
 * }</pre>
 *
 * <h3>Comparison to alternatives</h3>
 *
 * <p><b>Note:</b> In some cases the differences explained below can be subtle. When it's unclear
 * which approach to use, <b>don't worry</b> too much about it; just pick something that seems
 * reasonable and it will be fine.
 *
 * <ul>