class_docstr: The configuration class as stored in the ModelGraph.
  
  Args:
      config (dict):  The configuration dictionary
code: def __init__(self, config):
      self.config = config
      self.backend = get_backend(self.config.get("Backend", "Vivado"))
  
      self.model_precision = {}
      self.layer_type_precision = {}
      self.layer_name_precision = {}
  
      self.model_rf = None
      self.layer_type_rf = {}
      self.layer_name_rf = {}
  
      self.model_targ_cycles = None
      self.layer_type_targ_cycles = {}
      self.layer_name_targ_cycles = {}
  
      self.model_strategy = "Latency"
      self.layer_type_strategy = {}
      self.layer_name_strategy = {}
  
      self.model_conv_implementation = "LineBuffer"
      self.layer_type_conv_implementation = {}
      self.layer_name_conv_implementation = {}
  
      self.model_compression = False
      self.layer_type_compression = {}
      self.layer_name_compression = {}
  
      self.trace_output = self.get_config_value("TraceOutput", False)
  
      self.pipeline_style = "pipeline"
  
      self._parse_hls_config()
      self._validate_hls_config()
  
imports: import ctypes
  import os
  import platform
  from collections import OrderedDict
  import numpy
  import numpy.ctypeslib
  from hls4ml.backends import get_backend
  from hls4ml.model.flow import get_flow
  from hls4ml.model.layers import layer_map
  from hls4ml.model.optimizer import get_available_passes, optimize_model
label_desc: Use image node_id 1 to create a new HLSConfig object  with example: obj = HLSConfig(config)