class_docstr: Returns a classifier block without an activation function at the top.
  It consists of a 1 * 1 convolutional layer which maps the input to a num_class channel feature map.
  The output is a probability map for each of the classes.
  
  Args:
      spatial_dims: number of spatial dimensions.
      in_channels: number of input channels.
      out_channels: number of classes to map to.
      strides: convolution stride. Defaults to 1.
      kernel_size: convolution kernel size. Defaults to 3.
      adn_ordering: a string representing the ordering of activation, normalization, and dropout.
      Defaults to "NDA".
      act: activation type and arguments. Defaults to PReLU.
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code: def __init__(
      self,
      spatial_dims,
      in_channels,
      out_channels,
      strides,
      kernel_size,
      act=None,
      adn_ordering="A",
  ):
      super().__init__(
          spatial_dims,
          in_channels,
          out_channels,
          strides,
          kernel_size,
          adn_ordering,
          act,
      )
  
imports: from __future__ import annotations
  from typing import Optional, Sequence, Tuple, Union
  import torch
  import torch.nn
  import torch.nn.functional
  from monai.networks.blocks import ConvDenseBlock, Convolution
  from monai.networks.blocks import squeeze_and_excitation
  from monai.networks.layers.factories import Act, Norm
  from monai.networks.layers.simplelayers import SkipConnection
  from monai.networks.layers.utils import get_dropout_layer, get_pool_layer
  from monai.utils import optional_import
label_desc: Use image node_id 1 to create a new ClassifierBlock object from inherited base classes: Convolution with example: obj = ClassifierBlock(spatial_dims, in_channels, out_channels, strides, kernel_size, act, adn_ordering)