def get_param(module, layer_name, param_config):
    """
    Given a quantization configuration, get the right parameter
    for the module to be quantized.

    Args:
        - module: a nn.Module
        - layer_name: the name of the layer
        - param_config: a dict like
          {
              'Conv2d': ('kernel_size', {'(3, 3)': 9, '(1, 1)': 4}),
              'Linear': ('in_features', {'*': 8})
          }
          For instance, all conv2d layers with kernel size 3x3 have
          a block size of 9 and all Linear layers are quantized with
          a block size of 8, irrespective of their size.

    Remarks:
        - if 'fuzzy_name' is passed as a parameter, layers whose layer_name
          include 'fuzzy_name' will be assigned the given parameter.
          In the following example, conv.expand layers will have a block
          size of 9 while conv.reduce will have a block size of 4 and all
          other layers will have a block size of 2.
          {
              'Conv2d': ('fuzzy_name', {'expand': 9, 'reduce': 4, '*': 2}),
              'Linear': ('fuzzy_name', {'classifier': 8, 'projection': 4})
          }

    """

    layer_type = module.__class__.__name__

    if layer_type not in param_config:
        raise KeyError(f"Layer type {layer_type} not in config for layer {module}")

    feature, params = param_config[module.__class__.__name__]

    if feature != "fuzzy_name":
        feature_value = str(getattr(module, feature))
        if feature_value not in params:
            if "*" in params:
                feature_value = "*"
            else:
                raise KeyError(
                    f"{feature}={feature_value} not in config for layer {module}"
                )
    else:
        feature_values = [name for name in params if name in layer_name]
        if len(feature_values) == 0:
            if "*" in params:
                feature_value = "*"
            else:
                raise KeyError(f"name={layer_name} not in config for {module}")
        else:
            feature_value = feature_values[0]

    return params[feature_value]

    Given a quantization configuration, get the right parameter
    for the module to be quantized.

    Args:
        - module: a nn.Module
        - layer_name: the name of the layer
        - param_config: a dict like
          {
              'Conv2d': ('kernel_size', {'(3, 3)': 9, '(1, 1)': 4}),
              'Linear': ('in_features', {'*': 8})
          }
          For instance, all conv2d layers with kernel size 3x3 have
          a block size of 9 and all Linear layers are quantized with
          a block size of 8, irrespective of their size.

    Remarks:
        - if 'fuzzy_name' is passed as a parameter, layers whose layer_name
          include 'fuzzy_name' will be assigned the given parameter.
          In the following example, conv.expand layers will have a block
          size of 9 while conv.reduce will have a block size of 4 and all
          other layers will have a block size of 2.
          {
              'Conv2d': ('fuzzy_name', {'expand': 9, 'reduce': 4, '*': 2}),
              'Linear': ('fuzzy_name', {'classifier': 8, 'projection': 4})
          }

    
understand_icl/fairseq/fairseq/modules/quantization/pq/utils.py