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=[

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.

      }
    }
  }
}

// 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.

    *   Added reference implementation for 16-bit int and 32-bit unsigned int unquantized `mul`.
    *   `add_op` supports broadcasting up to 6 dimensions.
    *   Added 16-bit support for `top_k`.

*   `tf.function`

                               /*outfeed_config=*/rewriter.getStringAttr(""));
    rewriter.eraseOp(op);
    return success();
  }
};

// Converts tf.TopKV2 to chlo.top_k.
class ConvertTopKV2Op : public OpRewritePattern<TF::TopKV2Op> {
 public:
  using OpRewritePattern::OpRewritePattern;

  LogicalResult matchAndRewrite(TF::TopKV2Op op,

    backend_config = {
      aggregate_to_topk = true,
      is_fallback = true,
      recall_target = 8.500000e-01 : f32,
      reduction_dim = 1 : i64,
      reduction_input_size_override = -1 : i64,
      top_k = 4 : i64}
    } : (tensor<1x4xf32>, tensor<1x4xi32>, tensor<f32>, tensor<i32>) -> (tensor<1x4xf32>, tensor<1x4xi32>)
  func.return %0#0, %0#1 : tensor<1x4xf32>, tensor<1x4xi32>
}

func.func @topk_v2(%input: tensor<16x16xf32>) -> (tensor<16x8xf32>, tensor<16x8xi32>) {
  %k = "tf.Const"() {value = dense<8> : tensor<i32>} : () -> tensor<i32>

  // CHECK:     chlo.top_k(%[[INPUT]], k = 8)
  %0:2 = "tf.TopKV2"(%input, %k): (tensor<16x16xf32>, tensor<i32>) -> (tensor<16x8xf32>, tensor<16x8xi32>)
  func.return %0#0, %0#1: tensor<16x8xf32>, tensor<16x8xi32>
}

    checkNotNull(comparator);
    return Collector.of(
        () -> TopKSelector.<T>least(k, comparator),
        TopKSelector::offer,
        TopKSelector::combine,
        TopKSelector::topK,
        Collector.Characteristics.UNORDERED);
  }

  /**
   * Returns a {@code Collector} that returns the {@code k} greatest (relative to the specified

        {
          "datasource": {
            "type": "prometheus",
            "uid": "${DS_PROMETHEUS}"
          },
          "exemplar": true,
          "expr": "topk(1, sum(minio_cluster_capacity_usable_total_bytes{job=~\"$scrape_jobs\"}) by (instance)) - topk(1, sum(minio_cluster_capacity_usable_free_bytes{job=~\"$scrape_jobs\"}) by (instance))",
          "format": "time_series",
          "instant": false,
          "interval": "1m",