assertNotNull(uncPath, "UNC path should not be null");
        log.info("UNC path: {}", uncPath);
        // Be more flexible about UNC path format - different implementations may vary
        assertTrue(uncPath.contains("shared") || uncPath.contains("unc.txt"),
                "UNC path should contain either share name or file name, got: " + uncPath);
    }

        mean, var = tf.nn.moments(self.kernel, axes=[0, 1, 2], keepdims=True)
        return self.convolution_op(inputs, (self.kernel - mean) / tf.sqrt(var +
        1e-10))` Alternatively, you can override `convolution_op`: `python class
        StandardizedConv2D(tf.keras.Layer): def convolution_op(self, inputs,
        kernel): mean, var = tf.nn.moments(kernel, axes=[0, 1, 2],

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        // Test multiple replacements in same string
        System.setProperty("var1", "value1");
        System.setProperty("var2", "value2");

        String value = "${var1}-${var2}";
        assertEquals("value1-value2", ResourceUtil.resolve(value));

        // Test nested-like patterns (but not actual nesting)
        value = "${var1${var2}}"; // The ${var2} part gets replaced first

* ...etc.

Nevertheless, you might have a very specific use case where you really need to disable the API docs for some environment (e.g. for production) or depending on configurations from environment variables.

## Conditional OpenAPI from settings and env vars { #conditional-openapi-from-settings-and-env-vars }

///

An environment variable (also known as "**env var**") is a variable that lives **outside** of the Python code, in the **operating system**, and could be read by your Python code (or by other programs as well).

Environment variables could be useful for handling application **settings**, as part of the **installation** of Python, etc.

## Create and Use Env Vars { #create-and-use-env-vars }

```console
$ pip install "fastapi[all]"
---> 100%
```

</div>

### Create the `Settings` object { #create-the-settings-object }

Import `BaseSettings` from Pydantic and create a sub-class, very much like with a Pydantic model.

The same way as with Pydantic models, you declare class attributes with type annotations, and possibly default values.

		return
	}

	idpCfgType := mux.Vars(r)["type"]
	cfgName := mux.Vars(r)["name"]
	password := cred.SecretKey

	if !madmin.ValidIDPConfigTypes.Contains(idpCfgType) {
		writeErrorResponseJSON(ctx, w, errorCodes.ToAPIErr(ErrAdminConfigInvalidIDPType), r.URL)
		return
	}

	cfg := globalServerConfig.Clone()
	var cfgInfos []madmin.IDPCfgInfo
	var err error
	switch idpCfgType {

			} else {
				builder.AddVar(builder, expr.Vars[idx])
			}

			idx++
		} else {
			if v == '(' {
				afterParenthesis = true
			} else {
				afterParenthesis = false
			}
			builder.WriteByte(v)
		}
	}

	if idx < len(expr.Vars) {
		for _, v := range expr.Vars[idx:] {
			builder.AddVar(builder, sql.NamedArg{Value: v})
		}
	}
}

// NamedExpr raw expression for named expr

	}

	if !reflect.DeepEqual([]interface{}{"jinzhu", "POINT(100 100)"}, stmt.Vars) {
		t.Errorf("generated vars is not equal, got %v", stmt.Vars)
	}

	stmt = dryRunDB.Model(UserWithPoint{}).Create(map[string]interface{}{
		"Name":  "jinzhu",
		"Point": clause.Expr{SQL: "ST_PointFromText(?)", Vars: []interface{}{"POINT(100 100)"}},
	}).Statement