for _, queryClause := range association.Relationship.JoinTable.QueryClauses {
				joinStmt.AddClause(queryClause)
			}
			joinStmt.Build("WHERE")
			if len(joinStmt.SQL.String()) > 0 {
				tx.Clauses(clause.Expr{SQL: strings.Replace(joinStmt.SQL.String(), "WHERE ", "", 1), Vars: joinStmt.Vars})
			}
		}

        {
            "url": "http://example.com/dave.jpg",
            "name": "The Baz"
        }
    ]
}
```

/// info | Info

Beachten Sie, dass der `images`-Schlüssel jetzt eine Liste von Bild-Objekten hat.

///

## Tief verschachtelte Modelle { #deeply-nested-models }

Sie können beliebig tief verschachtelte Modelle definieren:

func (b *Buffer) AvailableBuffer() []byte { return b.buf[len(b.buf):] }

// String returns the contents of the unread portion of the buffer
// as a string. If the [Buffer] is a nil pointer, it returns "<nil>".
//
// To build strings more efficiently, see the [strings.Builder] type.
func (b *Buffer) String() string {
	if b == nil {
		// Special case, useful in debugging.
		return "<nil>"
	}
	return string(b.buf[b.off:])
}

                                          const char* name) {
  const auto& m = reader->GetVariableToShapeMap();
  return m.at(name).dims();
}

// This builder is used in the eager API to build a NodeDef.
struct TF_AttrBuilder : public tensorflow::AttrBuilder {
  using tensorflow::AttrBuilder::AttrBuilder;
  // The string buffers to make sure that any `attr_name` we pass into

// variables.
//
// The returned TF_WhileParams will have all fields initialized except
// `cond_output`, `body_outputs`, and `name`. The `body_outputs` buffer will be
// allocated to size `ninputs`. The caller should build `cond_graph` and
// `body_graph` starting from the inputs, and store the final outputs in
// `cond_output` and `body_outputs`.
//
// If `status` is OK, the caller must call either TF_FinishWhile or

                                                     const TF_DataType* values,
                                                     int num_values);

// Checks the tensorflow::NodeDef built via the methods above to see if it can
// run on device_type.
TF_CAPI_EXPORT extern void TF_AttrBuilderCheckCanRunOnDevice(
    TF_AttrBuilder* builder, const char* device_type, TF_Status* status);

/// info

This requires installing `ujson` for example with `pip install ujson`.

///

/// warning

`ujson` is less careful than Python's built-in implementation in how it handles some edge-cases.

///

{* ../../docs_src/custom_response/tutorial001_py39.py hl[2,7] *}

/// tip

It's possible that `ORJSONResponse` might be a faster alternative.

### **4.2 WordCount**

WordCount is a simple program that counts how often a word occurs in a text file. The code builds a dataset of (String, Int) pairs called counts, and saves the dataset to a file.

The following example submits WordCount code to the Scala shell. Select an input file for the Spark WordCount example. We can use any text file as input.

        else -> binaryName.replace('$', '.')
    }


/**
 * See https://kotlinlang.org/docs/reference/java-interop.html#mapped-types
 */
private
val mappedTypeStrings =
    mapOf(
        // Built-ins
        "java.lang.Cloneable" to "kotlin.Cloneable",
        "java.lang.Comparable" to "kotlin.Comparable",
        "java.lang.Enum" to "kotlin.Enum",
        "java.lang.annotation.Annotation" to "kotlin.Annotation",

		// To maintain the invariance that lastRefreshedAt records the
		// timestamp of last successful refresh
		config.lastRefreshedAt = UTCNow()
		return nil
	default:
		return err
	}

	// Reset drivercache built using current config
	clear(config.drivercache)
	// Remove existing tier configs
	clear(config.Tiers)
	// Copy over the new tier configs
	maps.Copy(config.Tiers, newConfig.Tiers)
	config.lastRefreshedAt = UTCNow()