class_docstr: Resample feature map for downsampling or upsampling.
code: def __init__(
      self,
      feat_level,
      target_num_channels,
      apply_bn=False,
      conv_after_downsample=False,
      data_format=None,
      pooling_type=None,
      upsampling_type=None,
      name="resample_p0",
  ):
      super().__init__(name=name)
      self.apply_bn = apply_bn
      self.data_format = data_format
      self.target_num_channels = target_num_channels
      self.feat_level = feat_level
      self.conv_after_downsample = conv_after_downsample
      self.pooling_type = pooling_type or "max"
      self.upsampling_type = upsampling_type or "nearest"
  
      self.conv2d = tf.keras.layers.Conv2D(
          self.target_num_channels,
          (1, 1),
          padding="same",
          data_format=self.data_format,
          name="conv2d",
      )
      self.bn = utils.build_batch_norm(
          data_format=self.data_format, name="bn"
      )
  
imports: import functools
  from absl import logging
  import numpy
  import tensorflow
  import utils
  from .None import fpn_configs
label_desc: Use image node_id 1 to create a new ResampleFeatureMap object from inherited base classes: tf.keras.layers with example: obj = ResampleFeatureMap(feat_level, target_num_channels, apply_bn, conv_after_downsample, data_format, pooling_type, upsampling_type, name)