"'h_scale' type: 'float'} attr : { name: 'max_classes_per_detection' "
    "type: 'int'} attr : { name: 'max_detections' type: 'int'} attr : { "
    "name: 'nms_iou_threshold' type: 'float'} attr : { name: "
    "'nms_score_threshold' type: 'float'} attr : { name: 'num_classes' type: "
    "'int'} attr : { name: 'w_scale' type: 'float'} attr : { name: 'x_scale' "

    Como esse arquivo **não muda com frequência**, o Docker irá detectá-lo e usar o **cache** para esse passo, habilitando o cache para o próximo passo também.

4. Instale as dependências de pacote vindas do arquivo de requisitos.

			// or it exist but doesn't implement the scale subresource. We check which
			// situation we are facing so we can give an appropriate error message.
			isScale, err := dc.implementsScale(mapping.Resource)
			if err != nil {
				return nil, err
			}
			if !isScale {
				return nil, fmt.Errorf("%s does not implement the scale subresource", gr.String())
			}
			return nil, nil
		}
		return nil, err
	}

f32], _support_output_type_float_in_quantized_op = true, detections_per_class = 100 : i64, device = "", h_scale = 5.000000e+00 : f32, max_classes_per_detection = 1 : i64, max_detections = 20 : i64, nms_iou_threshold = 6.000000e-01 : f32, nms_score_threshold = 3.000000e-01 : f32, num_classes = 90 : i64, use_regular_nms = false, w_scale = 5.000000e+00 : f32, x_scale = 1.000000e+01 : f32, y_scale = 1.000000e+01 : f32} : (tensor<1x64x64x32xf32>, tensor<1x64x64x32xf32>, tensor<1x64x64x32xf32>) -> (te...

    Value result = reduction.getResult(0);

    // The mean op needs to divide by the product of the reduced dimensions.
    if (std::is_same<OpTy, TF::MeanOp>::value) {
      Value in_shape = rewriter.create<shape::ShapeOfOp>(loc, op.getInput());
      Value divisor_count = rewriter.create<arith::ConstantIndexOp>(loc, 1);
      for (size_t i = 0; i < input_shape.size(); ++i) {

  typedef QuantizationParametersT NativeTableType;
  typedef QuantizationParametersBuilder Builder;
  enum FlatBuffersVTableOffset FLATBUFFERS_VTABLE_UNDERLYING_TYPE {
    VT_MIN = 4,
    VT_MAX = 6,
    VT_SCALE = 8,
    VT_ZERO_POINT = 10,
    VT_DETAILS_TYPE = 12,
    VT_DETAILS = 14,
    VT_QUANTIZED_DIMENSION = 16
  };
  const ::flatbuffers::Vector<float> *min() const {