code: def parse_reshape_layer(
      operation,
      layer_name,
      input_names,
      input_shapes,
      node,
      class_object,
      data_reader,
      config,
  ):
      assert operation == "View"
  
      layer = {}
      layer["class_name"] = "Reshape"
      layer["name"] = layer_name
      layer["inputs"] = input_names
  
      layer["target_shape"] = [int(i) for i in node.args[1:]]
      # View can have -1 as one as the dimensions,
      # leaving it to us to deduce it from the other dimensions and the overall size
      if -1 in layer["target_shape"]:
          size = np.prod(input_shapes[0][1:])
          for i in range(0, len(layer["target_shape"])):
              if layer["target_shape"][i] == -1:
                  cl = layer["target_shape"][:]
                  cl.remove(-1)
                  layer["target_shape"][i] = int(size / np.prod(cl))
  
      output_shape = input_shapes[0][:1] + layer["target_shape"]
  
      return layer, output_shape
  
imports: import numpy
  from hls4ml.converters.pytorch_to_hls import pytorch_handler
label_desc: Use image node_id 1 for calling a global function with example usage: parse_reshape_layer(operation, layer_name, input_names, input_shapes, node, class_object, data_reader, config) and returns: layer,  output_shape