code: def __init__(
      self,
      in_channel: int,
      out_channel: int,
      kernel_size: int,
      padding: int,
      p3dmode: int = 0,
      act_name: tuple | str = "RELU",
      norm_name: tuple | str = ("INSTANCE", {"affine": True}),
  ):
      super().__init__(
          in_channel,
          out_channel,
          kernel_size,
          padding,
          p3dmode,
          act_name,
          norm_name,
      )
      # 1 in_channel (activation) + 1 in_channel (convolution) +
      # 1 out_channel (convolution) + 1 out_channel (normalization)
      self.ram_cost = 2 + 2 * in_channel / out_channel
  
imports: from __future__ import annotations
  import datetime
  import warnings
  from typing import Optional
  import numpy
  import torch
  import torch.nn
  import torch.nn.functional
  from monai.networks.blocks.dints_block import ActiConvNormBlock, FactorizedIncreaseBlock, FactorizedReduceBlock, P3DActiConvNormBlock
  from monai.networks.layers.factories import Conv
  from monai.networks.layers.utils import get_act_layer, get_norm_layer
  from monai.utils import optional_import
label_desc: Use image node_id 1 to create a new _P3DActiConvNormBlockWithRAMCost object from inherited base classes: P3DActiConvNormBlock with example: obj = _P3DActiConvNormBlockWithRAMCost(in_channel, out_channel, kernel_size, padding, p3dmode, act_name, norm_name)