Project: 036_HiRA_Parameter-Efficient_Hadamard_High-Rank_Adapta | Function: update_layer (iclr)

system: You are an expert Python programmer implementing algorithms from scientific papers.
Complete the masked function based on the algorithm description.
Follow Python best practices and maintain consistent code style.
Return **only** the complete Python function code.

user: Complete this Python function:

**DESCRIPTION:** Initializes and configures HiRA adaptation layer with low-rank matrices A and B, setting up the trainable parameters and scaling factors according to the HiRA methodology

**KEY TERMS:** {
  "r_ab": "r rank parameter from paper",
  "lora_A": "A matrix from decomposition",
  "lora_B": "B matrix from decomposition",
  "scaling_ab": "scaling factor for AB product"
}

**CONTEXT CODE:**         pass
    elif bias == "all":
        for n, p in model.named_parameters():
            if "bias" in n:
                p.requires_grad = True
    elif bias == "lora_only":
        for m in model.modules():
            if isinstance(m, LoraLayer) and hasattr(m, "bias") and m.bias is not None:
                m.bias.requires_grad = True
    else:
        raise NotImplementedError
    if config:
        for n, p in model.named_parameters():
            if "lora_" in n:
                p.requires_grad = False
        if config.train_a:
            for n, p in model.named_parameters():
                if "lora_A" in n:
                    p.requires_grad = True
        if config.train_b:
            for n, p in model.named_parameters():
                if "lora_B" in n:
                    p.requires_grad = True


class LoraLayer:
    def __init__(
            self,
            in_features: int,
            out_features: int,
    ):
        self.r_ab = {}
        self.lora_alpha = {}
        self.scaling_ab = {}
        self.lora_dropout = nn.ModuleDict({})
        self.lora_A = nn.ParameterDict({})
        self.lora_B = nn.ParameterDict({})
        # For Embedding layer
        self.lora_embedding_A = nn.ParameterDict({})
        self.lora_embedding_B = nn.ParameterDict({})
        # Mark the weight as unmerged
        self.merged = False
        self.disable_adapters = False
        self.in_features = in_features
        self.out_features = out_features
        self.init_a = None
        self.init_b = None
        self.rand_R = False
        self.lora_R = nn.ParameterDict({})

    def update_layer(self, adapter_name, r_ab, lora_alpha, lora_dropout, init_lora_weights, scale_ab, init_a,
                     init_b, rand_R):
        raise NotImplementedError("Function implementation has been masked for testing")
    
    def reset_lora_parameters(self, adapter_name):
        raise NotImplementedError("Function implementation has been masked for testing")
    
class Linear(nn.Linear, LoraLayer):
    # Lora implemented in a dense layer
    def __init__(
            self,
            adapter_name: str,
            in_features: int,
            out_features: int,
            r_ab: int = 0,
            lora_alpha: int = 1,
            lora_dropout: float = 0.0,
            fan_in_fan_out: bool = False,
            # Set this to True if the layer to replace stores weight like (fan_in, fan_out)
            scale_ab: float = 1.0,
            init_a: str = 'zero',
            init_b: str = 'zero',
            train_a: bool = True,
            train_b: bool = True,
            rand_R: bool = False,
            **kwargs,
    ):
        init_lora_weights = kwargs.pop("init_lora_weights", True)
        nn.Linear.__init__(self, in_features, out_features, **kwargs)
        LoraLayer.__init__(self, in_features=in_features, out_features=out_features)
        # Freezing the pre-trained weight matrix
        self.weight.requires_grad = False
        self.train_a = train_a
        self.train_b = train_b
        self.rand_R = rand_R
        self.fan_in_fan_out = fan_in_fan_out
        if fan_in_fan_out:
            self.weight.data = self.weight.data.T

        nn.Linear.reset_parameters(self)
        self.update_layer(adapter_name, r_ab, lora_alpha, lora_dropout, init_lora_weights, scale_ab,
                          init_a, init_b, rand_R)
        self.active_adapter = adapter_name

    def merge(self):
        raise NotImplementedError(' need reimplementation!')

    def unmerge(self):
        if self.active_adapter not in self.lora_A.keys():
            return
        if not self.merged:
            warnings.warn("Already unmerged. Nothing to do.")
            return
        if self.r[self.active_adapter] > 0:
            self.weight.data -= (
                    transpose(
                        self.lora_B[self.active_adapter] @ self.lora_A[self.active_adapter],
                        self.fan_in_fan_out,
                    )
                    * self.scaling[self.active_adapter]
            )
            self.merged = False

    def forward(self, x: torch.Tensor):
        raise NotImplementedError("Function implementation has been masked for testing")

Complete the function implementation replacing "raise NotImplementedError".