code: def init_conv1d(self, layer):
      # This can happen if we assign weights of Dense layer to 1x1 Conv1D
      if len(layer.weights["weight"].data.shape) == 2:
          layer.weights["weight"].data = np.expand_dims(
              layer.weights["weight"].data, axis=(0, 1)
          )
  
      # Dense matrix multiply properties
      layer.set_attr("rfpad", 0)
      layer.set_attr("bfpad", 0)
  
      # Reuse and parallelization factors
      layer.set_attr("strategy", "resource")
      n_in, n_out = self.get_layer_mult_size(layer)
      self.set_target_reuse_factor(layer)
      self.set_closest_reuse_factor(layer, n_in, n_out)
      layer.set_attr(
          "parallelization",
          layer.model.config.get_layer_config_value(
              layer, "ParallelizationFactor", 1
          ),
      )
  
      # impl_filt_width determines the filter size post-Winograd transformation
      layer.set_attr("impl_filt_width", layer.get_attr("filt_width"))
  
      # Implementation:
      # - combination - at compile-time, the decision between Winograd and im2col is made
      # - im2col - specifically use im2col
      # - Winograd - use Winograd, if possible
      layer.set_attr(
          "implementation",
          layer.model.config.get_layer_config_value(
              layer, "Implementation", "combination"
          ),
      )
  
      layer.set_attr("n_partitions", 1)
  
imports: import os
  from contextlib import contextmanager
  import numpy
  from hls4ml.backends import FPGABackend
  from hls4ml.model.attributes import ConfigurableAttribute, TypeAttribute
  from hls4ml.model.flow import register_flow
  from hls4ml.model.layers import GRU, LSTM, Activation, Conv1D, Conv2D, Dense, Embedding, Layer, SimpleRNN, Softmax
  from hls4ml.model.optimizer import get_backend_passes, layer_optimizer
  from hls4ml.model.types import FixedPrecisionType, IntegerPrecisionType, NamedType
  from hls4ml.report import parse_quartus_report
label_desc: Use image node_id 14 for calling the QuartusBackend obj's underlying member method code with example usage: obj.init_conv1d(layer) without return types