x = math_ops.matmul(a, b, name='a_b')
    y = math_ops.add(x, z)
  array_ops.identity(y, name='result')


def tftop_k(_):
  x = array_ops.placeholder(dtypes.int32, shape=[5], name='x')
  output = nn_ops.top_k(x, 2, name='values')
  array_ops.identity(output[1], name='indices')


def tfvariable_readonly(_):
  x = variables.Variable(1000.0, name='x')
  unused_y = variables.Variable(1000.0, name='y')
  old_x = x.value()

    if (!backend_config) {
      return op.emitOpError() << "Missing backend_config attribute";
    }

    for (const auto& attr : backend_config) {
      auto name = attr.getName();
      if (!(name == "top_k" || name == "reduction_dim" ||
            name == "recall_target" || name == "aggregate_to_topk" ||
            name == "reduction_input_size_override" || name == "is_fallback")) {
        return op.emitOpError()

      )
      def multiple_output_ops(
          self, input_tensor: core.Tensor
      ) -> Mapping[str, core.Tensor]:
        k = array_ops.constant(4, dtype=dtypes.int32)
        values, indices = nn_ops.top_k(input_tensor, k, name='TopK')
        adj_values = values + 2
        return {'indices': indices, 'adj_values': adj_values, 'values': values}

      @def_function.function(
          input_signature=[

      top.offer(i);
    }
    assertThat(top.topK()).isEmpty();
  }

  public void testNoElementsOffered() {
    TopKSelector<Integer> top = TopKSelector.least(10);
    assertThat(top.topK()).isEmpty();
  }

  public void testOfferedFewerThanK() {
    TopKSelector<Integer> top = TopKSelector.least(10);
    top.offer(3);
    top.offer(5);
    top.offer(2);
    assertThat(top.topK()).containsExactly(2, 3, 5).inOrder();
  }

      threshold = buffer[k - 1];
    }
    // Up to bufferSize, all elements of buffer are real Ts (not null unless T includes null)
    T[] topK = Arrays.copyOf(castBuffer, bufferSize);
    // we have to support null elements, so no ImmutableList for us
    return Collections.unmodifiableList(Arrays.asList(topK));
  }

// CHECK-SAME: -> (tensor<?xf32> {tf_saved_model.index_path = ["TopK:0"]}, tensor<?xi32> {tf_saved_model.index_path = ["TopK:1"]})
// CHECK-SAME: attributes {tf.entry_function = {inputs = "input:0,k:0", outputs = "TopK:0,TopK:1"}, tf_saved_model.exported_names = ["main"]}
// CHECK: %[[CALL0:.*]]:2 = "tf.PartitionedCall"(%arg0, %arg1) <{config = "", config_proto = "", executor_type = "", f = @topk}>
// Expects an IdentityN op to be created.

Tutorial ini menunjukan cara menggunakan ***FastAPI*** dengan semua fitur-fiturnya, tahap demi tahap.

Setiap bagian dibangun secara bertahap dari bagian sebelumnya, tetapi terstruktur untuk memisahkan banyak topik, sehingga kamu bisa secara langsung menuju ke topik spesifik untuk menyelesaikan kebutuhan API tertentu.

Ini juga dibangun untuk digunakan sebagai referensi yang akan datang.

Sehingga kamu dapat kembali lagi dan mencari apa yang kamu butuhkan dengan tepat.

      threshold = buffer[k - 1];
    }
    // Up to bufferSize, all elements of buffer are real Ts (not null unless T includes null)
    T[] topK = Arrays.copyOf(castBuffer, bufferSize);
    // we have to support null elements, so no ImmutableList for us
    return Collections.unmodifiableList(Arrays.asList(topK));
  }

// CHECK:      %[[OP_G:[a-z0-9]*]] = "tf.opG"
// CHECK:      %[[OP_H:[a-z0-9]*]] = "tf.opH"
// CHECK:      %[[OP_I:[a-z0-9]*]] = "tf.opI"
// CHECK:      %[[OP_J:[a-z0-9]*]] = "tf.opJ"
// CHECK:      %[[OP_K:[a-z0-9]*]] = "tf.opK"
// CHECK:      %[[OP_L:[a-z0-9]*]] = "tf.opL"
// CHECK:      tf_device.replicate
// CHECK-SAME: [%[[OP_A]], %[[OP_B]], %[[OP_C]]] as %{{[a-z0-9]*}}: tensor<*xi1>

      }
    }
  }
}

// Information to identify an output in its node and in the model output list.
// Ex: If the model output list is ["add:0", "topk:0": "topk:1"], then the
// output corresponding to "topk:1" will have output_index=2 and tensor_index=1.
struct OutputInfo {
  // The index of this output in the model output list.
  int32_t output_index;
  // The index of this output in its node.