confs += it
                        }
                    }
                    def attrs = [name: art.name, type: art.type, ext: art.ext, conf: confs.join(',')]
                    if (art.classifier) {
                        attrs["m:classifier"] = art.classifier
                    }
                    builder.artifact(attrs)
                }
            }
        }

  ^bb0(%arg4: tensor<4xf32>):
    %1 = func.call @init(%arg4) : (tensor<4xf32>) -> tensor<7xf32>
    "tf.Yield"(%1) : (tensor<7xf32>) -> ()
  }, {
  ^bb0(%arg4: tensor<7xf32>, %arg5: tensor<3xf32>):
    %1 = func.call @next(%arg4, %arg5) : (tensor<7xf32>, tensor<3xf32>) -> tensor<6xf32>
    "tf.Yield"(%1) : (tensor<6xf32>) -> ()
  }, {
  ^bb0(%arg4: tensor<6xf32>, %arg5: tensor<2xf32>):

                      %arg2: tensor<!tf_type.resource<tensor<1x3x!tf_type.qint8>>>,
                      %arg3: tensor<!tf_type.resource<tensor<1x3x!tf_type.qint16>>>,
                      %arg4: tensor<!tf_type.resource<tensor<1x3x!tf_type.qint32>>>) {
  // CHECK: tf.ReadVariableOp
  // CHECK-SAME: dtype = !corert.quint8

        // TODO Auto-generated method stub
        return null;
    }

    @Override
    public String generateType3Msg(final String arg0, final String arg1, final String arg2, final String arg3, final String arg4)
            throws NTLMEngineException {
        // TODO Auto-generated method stub
        return null;
    }

Verwenden Sie einfach jede Art von Modell, jede Art von Klasse, jede Art von Datenbank, die Sie für Ihre Anwendung benötigen. **FastAPI** deckt das alles mit seinem Dependency Injection System ab.

## Codegröße

    %6 = mhlo.compare  GE, %arg1, %arg3 : (tensor<i32>, tensor<i32>) -> tensor<i1>
    %7 = mhlo.select %6, %arg1, %arg3 : tensor<i1>, tensor<i32>
    %8 = mhlo.compare  EQ, %arg1, %arg3 : (tensor<i32>, tensor<i32>) -> tensor<i1>
    %9 = mhlo.minimum %arg2, %arg4 : tensor<i32>
    %10 = mhlo.select %6, %arg2, %arg4 : tensor<i1>, tensor<i32>

func.func @decompose_resource_apply_adam_non_nesterov(%arg0: tensor<f32>, %arg1: tensor<f32>, %arg2: tensor<f32>, %arg3: tensor<f32>, %arg4: tensor<f32>, %arg5: tensor<f32>, %arg6: tensor<f32>) -> () {
  "tf_device.cluster"() ({

    // CHECK-DAG: [[ONE:%.*]] = "tf.Const"() <{value = dense<1.000000e+00> : tensor<f32>}>

// CHECK:     %2 = "tfl.range"(%arg3, %1, %arg4) {tac.device = "DARWINN", tac.inference_type = "FLOAT"} : (tensor<i32>, tensor<i32>, tensor<i32>) -> tensor<?xi32>
// CHECK:     %3 = "tfl.pack"(%1, %arg4) <{axis = 0 : i32, values_count = 2 : i32}> {tac.device = "DARWINN", tac.inference_type = "FLOAT"} : (tensor<i32>, tensor<i32>) -> tensor<2xi32>

  %0 = "tfl.transpose"(%arg0, %cst_1) : (tensor<4x4x3xf32>, tensor<3xi32>) -> tensor<4x4x3xf32>
  %1:6 = "tfl.while"(%cst_7, %cst_7, %cst_2, %cst, %cst, %0) ({
  ^bb0(%arg1: tensor<i32>, %arg2: tensor<i32>, %arg3: tensor<*xf32>, %arg4: tensor<4x2xf32>, %arg5: tensor<4x2xf32>, %arg6: tensor<*xf32>):
    %5 = "tfl.less"(%arg2, %cst_3) : (tensor<i32>, tensor<i32>) -> tensor<i1>
    %6 = "tfl.less"(%arg1, %cst_3) : (tensor<i32>, tensor<i32>) -> tensor<i1>

  func.return %4 : tensor<128x128x!quant.uniform<u8:f32, 0.2:125>>

// CHECK: "tfl.custom_tf"
// CHECK-NEXT: ^bb0(%arg4: tensor<128x128xf32>, %arg5: tensor<1xf32>, %arg6: tensor<1xf32>, %arg7: tensor<1xi32>):
// CHECK-NEXT:   "tf.LayerNorm"(%arg4, %arg5, %arg6, %arg7) {_tfl_quant_trait = "fully_quantizable", device = ""} : (tensor<128x128xf32>, tensor<1xf32>, tensor<1xf32>, tensor<1xi32>) -> tensor<128x128xf32>