class_docstr: This module implements a basic residual convolution block using MinkowskiEngine
  
  Parameters
  ----------
  inplanes: int
      Input dimension
  planes: int
      Output dimension
  dilation: int
      Dilation value
  downsample: nn.Module
      If provided, downsample will be applied on input before doing residual addition
  bn_momentum: float
      Input dimension
class_docstr_tok: ["This","module","implements","a","basic","residual","convolution","block","using","MinkowskiEngine","Parameters","--","--","--","--","--","inplanes",":","int","Input","dimension","planes",":","int","Output","dimension","dilation",":","int","Dilation","value","downsample",":","nn.Module","If","provided",",","downsample","will","be","applied","on","input","before","doing","residual","addition","bn_momentum",":","float","Input","dimension"]
code: def __init__(
      self,
      inplanes,
      planes,
      stride=1,
      dilation=1,
      downsample=None,
      bn_momentum=0.1,
      dimension=-1,
  ):
      super(BasicBlock, self).__init__()
      assert dimension > 0
  
      self.conv1 = ME.MinkowskiConvolution(
          inplanes,
          planes,
          kernel_size=3,
          stride=stride,
          dilation=dilation,
          dimension=dimension,
      )
      self.norm1 = ME.MinkowskiBatchNorm(planes, momentum=bn_momentum)
      self.conv2 = ME.MinkowskiConvolution(
          planes,
          planes,
          kernel_size=3,
          stride=1,
          dilation=dilation,
          dimension=dimension,
      )
      self.norm2 = ME.MinkowskiBatchNorm(planes, momentum=bn_momentum)
      self.relu = ME.MinkowskiReLU(inplace=True)
      self.downsample = downsample
  
imports: import torch.nn
  import MinkowskiEngine
  from .common import ConvType, NormType
  from torch_points3d.utils.config import is_list
label_desc: Use image node_id 1 to create a new BasicBlock object from inherited base classes: nn with example: obj = BasicBlock(inplanes, planes, stride, dilation, downsample, bn_momentum, dimension)