code: def __init__(
      self,
      num_out,
      kernel_size=1,
      strides=1,
      pad=0,
      num_groups=1,
      use_act=True,
      act_type="relu",
      prefix="",
      norm_layer=BatchNorm,
      **kwargs,
  ):
      super(_Unit, self).__init__(**kwargs)
      self.use_act = use_act
      self.conv = nn.Conv2D(
          channels=num_out,
          kernel_size=kernel_size,
          strides=strides,
          padding=pad,
          groups=num_groups,
          use_bias=False,
          prefix="%s-conv2d_" % prefix,
      )
      self.bn = norm_layer(prefix="%s-batchnorm_" % prefix)
      if use_act is True:
          self.act = Activation(act_type)
  
imports: from __future__ import division
  import numpy
  from mxnet.gluon import nn
  from mxnet.gluon.nn import BatchNorm
  from mxnet.gluon.block import HybridBlock
  from mxnet.context import cpu
  from ..nn import ReLU6, HardSigmoid, HardSwish
label_desc: Use image node_id 1 to create a new _Unit object from inherited base classes: HybridBlock with example: obj = _Unit(num_out, kernel_size, strides, pad, num_groups, use_act, act_type, prefix, norm_layer)