// CHECK-LABEL: testMultinomial
func.func @testMultinomial(%arg0: tensor<2xf32>, %arg1: tensor<1xi32>) -> tensor<10xi64> {
  // CHECK: "tfl.multinomial"(%arg0, %arg1)
  %0 = "tfl.multinomial"(%arg0, %arg1) {seed = 0 : i64, seed2 = 0: i64} : (tensor<2xf32>, tensor<1xi32>) -> tensor<10xi64>
  func.return %0 :  tensor<10xi64>
}

// -----

// CHECK-LABEL: testMultinomialInt32

///   %scaled = "mhlo.multiply"(%iota, %delta)
///       {broadcast_dimensions = dense<[]> : tensor<0xi64>} :
///       (tensor<5xf32>, tensor<f32>) -> tensor<5xf32>
///   %result = "mhlo.add"(%scaled, %offset)
///       {broadcast_dimensions = dense<[]> : tensor<0xi64>} :
///       (tensor<5xf32>, tensor<f32>) -> tensor<5xf32>
///

    SmallVector<Value> start_indices(rank);
    for (auto [i, start_index] : llvm::enumerate(op.getStartIndices())) {
      // Start indices should be casted from tensor<i64> to tensor<1xi64>.
      auto cast = rewriter.create<TFL::BitcastOp>(
          op->getLoc(), single_element_type, start_index);
      int64_t upper_limit_idx = operand_shape[i] - slice_sizes[i];
      auto upper_limit_attr =

  // CHECK:  return %1 : tensor<*xi32>
}

// CHECK-LABEL: @broadcast_to_ui32
func.func @broadcast_to_ui32(%arg0: tensor<ui32>, %arg1: tensor<1xi64>) -> tensor<10xui32> {
  %0 = "tfl.broadcast_to"(%arg0, %arg1) : (tensor<ui32>, tensor<1xi64>) -> tensor<10xui32>
  return %0 : tensor<10xui32>
  // CHECK:  %cst = arith.constant dense<1> : tensor<10xui32>

* 🌐 Add Japanese translation for `docs/ja/docs/deployment/server-workers.md`. PR [#10064](https://github.com/tiangolo/fastapi/pull/10064) by [@tamtam-fitness](https://github.com/tamtam-fitness).