class_docstr: This class stores the quantized linear block params. It is adapted from torchvision.models.regnet:
      https://github.com/pytorch/vision/blob/c06d52b1c5f6aee36802661c3ebc6347b97cc59e/torchvision/models/regnet.py#L203
  
  Args:
      depth: The total number of XBlocks in the network
      w_0: Initial width
      w_a: Width slope
      w_m: Width slope in the log space
      groups: The number of groups to use in the XBlock. Referred to
      se_ratio: The squeeze-excitation ratio. The number of channels in the SE module will be the
          input channels scaled by this ratio.
      bottleneck_multiplier: The number of output channels in the intermediate conv layers in bottleneck/Xblock
          block will be scaled by this value.
      quant: Block widths will be divisible by this value
      stride: The stride of the 3x3 conv of the XBlocks
code: def __repr__(self) -> str:
      return "{}({}\n)".format(
          self.__class__.__name__, self.extra_repr()
      )
  
file_path: ml-cvnets/cvnets/models/classification/config/regnet.py
imports: import argparse
  from dataclasses import dataclass
  from typing import Dict, List, Tuple, Union
  import numpy
  from utils import logger
  from utils.math_utils import make_divisible
label_desc: Use image node_id 4 for calling the BlockParamsConfig obj's underlying member method code with example usage: obj.__repr__() and returns: str