code: def __init__(
      self,
      param_id,
      param,
      key=None,
      shape=None,
      length=None,
      grad=None,
      blob_copy=None,
  ):
      assert isinstance(param, core.BlobReference)
      self.param_id = param_id
      self.name = str(param)
      self.blob = param
      self.key = key
      self.shape = shape
      self.size = None if shape is None else np.prod(shape)
      self.length = max(1, length if length is not None else 1)
      self.grad = grad
      self._cloned_init_net = None
      # Optionally store equivalent copies of the blob
      # in different precisions (i.e. half and float copies)
      # stored as a dict of TensorProto.DataType -> BlobReference
      self.blob_copy = blob_copy
      # each param_info can have its own optimizer. It can be set within
      # OptimizerContext (caffe2/python/optimizer.py)
      self._optimizer = None
  
file_path: pytorch/caffe2/python/modeling/parameter_info.py
imports: from caffe2.python import core
  import numpy
label_desc: Use image node_id 1 to create a new ParameterInfo object  with example: obj = ParameterInfo(param_id, param, key, shape, length, grad, blob_copy)