docstr: Blocks layer, which consist of some BaickBlock layers
  
  Args:
      ch_in (int): input channel
      ch_out (int): output channel
      count (int): number of BasicBlock layer
      norm_type (str): batch norm type, default bn
      norm_decay (str): decay for weight and bias of batch norm layer, default 0.
      freeze_norm (bool): whether to freeze norm, default False
      name (str): layer name
      data_format (str): data format, NCHW or NHWC
docstr_tok: ["Blocks","layer",",","which","consist","of","some","BaickBlock","layers","Args",":","ch_in","(","int",")",":","input","channel","ch_out","(","int",")",":","output","channel","count","(","int",")",":","number","of","BasicBlock","layer","norm_type","(","str",")",":","batch","norm","type",",","default","bn","norm_decay","(","str",")",":","decay","for","weight","and","bias","of","batch","norm","layer",",","default","0.","freeze_norm","(","bool",")",":","whether","to","freeze","norm",",","default","False","name","(","str",")",":","layer","name","data_format","(","str",")",":","data","format",",","NCHW","or","NHWC"]
code: def __init__(
      self,
      ch_in,
      ch_out,
      count,
      norm_type="bn",
      norm_decay=0.0,
      freeze_norm=False,
      name=None,
      data_format="NCHW",
  ):
      """
      Blocks layer, which consist of some BaickBlock layers
  
      Args:
          ch_in (int): input channel
          ch_out (int): output channel
          count (int): number of BasicBlock layer
          norm_type (str): batch norm type, default bn
          norm_decay (str): decay for weight and bias of batch norm layer, default 0.
          freeze_norm (bool): whether to freeze norm, default False
          name (str): layer name
          data_format (str): data format, NCHW or NHWC
      """
      super(Blocks, self).__init__()
  
      self.basicblock0 = BasicBlock(
          ch_in,
          ch_out,
          norm_type=norm_type,
          norm_decay=norm_decay,
          freeze_norm=freeze_norm,
          data_format=data_format,
      )
      self.res_out_list = []
      for i in range(1, count):
          block_name = "{}.{}".format(name, i)
          res_out = self.add_sublayer(
              block_name,
              BasicBlock(
                  ch_out,
                  ch_out,
                  norm_type=norm_type,
                  norm_decay=norm_decay,
                  freeze_norm=freeze_norm,
                  data_format=data_format,
              ),
          )
          self.res_out_list.append(res_out)
      self.ch_out = ch_out
  
imports: import paddle
  import paddle.nn
  import paddle.nn.functional
  from ppdet.core.workspace import register, serializable
  from ppdet.modeling.ops import batch_norm, mish
  from ..shape_spec import ShapeSpec
label_desc: Use image node_id 1 to create a new Blocks object from inherited base classes: nn with example: obj = Blocks(ch_in, ch_out, count, norm_type, norm_decay, freeze_norm, name, data_format)