def __init__(self, resnet_depth, dropblock=nn_ops.Dropblock(), batch_norm_relu=nn_ops.BatchNormRelu(), init_drop_connect_rate=None, data_format='channels_last'):
    'ResNet initialization function.\n\n    Args:\n      resnet_depth: `int` depth of ResNet backbone model.\n      dropblock: a dropblock layer.\n      batch_norm_relu: an operation that includes a batch normalization layer\n        followed by a relu layer(optional).\n      init_drop_connect_rate: a \'float\' number that specifies the initial drop\n        connection rate. Note that the default `None` means no drop connection\n        is applied.\n      data_format: `str` either "channels_first" for `[batch, channels, height,\n        width]` or "channels_last for `[batch, height, width, channels]`.\n    '
    self._resnet_depth = resnet_depth
    self._dropblock = dropblock
    self._batch_norm_relu = batch_norm_relu
    self._init_drop_connect_rate = init_drop_connect_rate
    self._data_format = data_format
    model_params = {10: {'block': nn_blocks.residual_block, 'layers': [1, 1, 1, 1]}, 18: {'block': nn_blocks.residual_block, 'layers': [2, 2, 2, 2]}, 34: {'block': nn_blocks.residual_block, 'layers': [3, 4, 6, 3]}, 50: {'block': nn_blocks.bottleneck_block, 'layers': [3, 4, 6, 3]}, 101: {'block': nn_blocks.bottleneck_block, 'layers': [3, 4, 23, 3]}, 152: {'block': nn_blocks.bottleneck_block, 'layers': [3, 8, 36, 3]}, 200: {'block': nn_blocks.bottleneck_block, 'layers': [3, 24, 36, 3]}}
    if (resnet_depth not in model_params):
        valid_resnet_depths = ', '.join([str(depth) for depth in sorted(model_params.keys())])
        raise ValueError(('The resnet_depth should be in [%s]. Not a valid resnet_depth:' % valid_resnet_depths), self._resnet_depth)
    params = model_params[resnet_depth]
    self._resnet_fn = self.resnet_v1_generator(params['block'], params['layers'])
ResNet initialization function.

Args:
  resnet_depth: `int` depth of ResNet backbone model.
  dropblock: a dropblock layer.
  batch_norm_relu: an operation that includes a batch normalization layer
    followed by a relu layer(optional).
  init_drop_connect_rate: a 'float' number that specifies the initial drop
    connection rate. Note that the default `None` means no drop connection
    is applied.
  data_format: `str` either "channels_first" for `[batch, channels, height,
    width]` or "channels_last for `[batch, height, width, channels]`.
def __init__(self, resnet_depth, dropblock=nn_ops.Dropblock(), batch_norm_relu=nn_ops.BatchNormRelu(), init_drop_connect_rate=None, data_format='channels_last'):
    'ResNet initialization function.\n\n    Args:\n      resnet_depth: `int` depth of ResNet backbone model.\n      dropblock: a dropblock layer.\n      batch_norm_relu: an operation that includes a batch normalization layer\n        followed by a relu layer(optional).\n      init_drop_connect_rate: a \'float\' number that specifies the initial drop\n        connection rate. Note that the default `None` means no drop connection\n        is applied.\n      data_format: `str` either "channels_first" for `[batch, channels, height,\n        width]` or "channels_last for `[batch, height, width, channels]`.\n    '
    self._resnet_depth = resnet_depth
    self._dropblock = dropblock
    self._batch_norm_relu = batch_norm_relu
    self._init_drop_connect_rate = init_drop_connect_rate
    self._data_format = data_format
    model_params = {10: {'block': nn_blocks.residual_block, 'layers': [1, 1, 1, 1]}, 18: {'block': nn_blocks.residual_block, 'layers': [2, 2, 2, 2]}, 34: {'block': nn_blocks.residual_block, 'layers': [3, 4, 6, 3]}, 50: {'block': nn_blocks.bottleneck_block, 'layers': [3, 4, 6, 3]}, 101: {'block': nn_blocks.bottleneck_block, 'layers': [3, 4, 23, 3]}, 152: {'block': nn_blocks.bottleneck_block, 'layers': [3, 8, 36, 3]}, 200: {'block': nn_blocks.bottleneck_block, 'layers': [3, 24, 36, 3]}}
    if (resnet_depth not in model_params):
        valid_resnet_depths = ', '.join([str(depth) for depth in sorted(model_params.keys())])
        raise ValueError(('The resnet_depth should be in [%s]. Not a valid resnet_depth:' % valid_resnet_depths), self._resnet_depth)
    params = model_params[resnet_depth]
    self._resnet_fn = self.resnet_v1_generator(params['block'], params['layers'])