code: def _darknet_dense(inputs, params, attrs, prefix):
      """Process the dense operation."""
      new_attrs = {}
      new_attrs["units"] = attrs.get("num_hidden")
      data = inputs[0]
  
      if attrs.get("use_flatten", False) is True:
          data = get_relay_op("batch_flatten")(data)
  
      weight = _get_param_var(params, prefix, "weight")
      data = get_relay_op("dense")(data, weight, **new_attrs)
  
      use_bias = attrs.get("use_bias", False)
      if use_bias:
          bias = _get_param_var(params, prefix, "bias")
          data = get_relay_op("bias_add")(data, bias, axis=1)
  
      if "use_batchNorm" in attrs:
          new_attrs = {}
          new_attrs["epsilon"] = 0.000001
          gamma = _get_param_var(params, prefix, "gamma")
          beta = _get_param_var(params, prefix, "beta")
          moving_mean = _get_param_var(params, prefix, "moving_mean")
          moving_var = _get_param_var(params, prefix, "moving_var")
          data = get_relay_op("batch_norm")(
              data, gamma, beta, moving_mean, moving_var, **new_attrs
          )
      if "activation" in attrs:
          new_attrs = {}
          new_attrs["activation"] = attrs["activation"]
          data = _darknet_activations(data, None, new_attrs)
      return data
  
imports: from enum import Enum
  import numpy
  import tvm
  from tvm.ir import IRModule
  from ..None import analysis
  from ..None import expr
  from ..None import function
  from .common import get_relay_op, new_var
label_desc: Use image node_id 9 for calling a global function with example usage: _darknet_dense(inputs, params, attrs, prefix) and returns: data