parameters: (self,
             lhs,
             rhs,
             window_strides,
             padding,
             feature_group_count=1,
             batch_group_count=1,
             precision_config=None)
return_statement: return self.ConvGeneralDilated(
          lhs,
          rhs,
          window_strides,
          pads, [], [],
          dimension_numbers=None,
          feature_group_count=feature_group_count,
          batch_group_count=batch_group_count,
          precision_config=precision_config)
docstring: Enqueues a Conv operation onto the computation.
  
      Args:
        lhs: XlaOp for the rank N+2 array of inputs.
        rhs: XlaOp for the rank N+2 array of kernel weights.
        window_strides: length-N array-like of integer kernel strides.
        padding: PaddingType representing either 'SAME' or 'VALID' padding.
        feature_group_count: number of feature groups for grouped convolution.
        batch_group_count: number of batch groups for grouped convolution.
      Returns: a XlaOp representing the Conv operation.
docstring_summary: Enqueues a Conv operation onto the computation.
docstring_tokens: Enqueues a Conv operation onto the computation .
function: def Conv(self,
             lhs,
             rhs,
             window_strides,
             padding,
             feature_group_count=1,
             batch_group_count=1,
             precision_config=None):
      """Enqueues a Conv operation onto the computation.
  
      Args:
        lhs: XlaOp for the rank N+2 array of inputs.
        rhs: XlaOp for the rank N+2 array of kernel weights.
        window_strides: length-N array-like of integer kernel strides.
        padding: PaddingType representing either 'SAME' or 'VALID' padding.
        feature_group_count: number of feature groups for grouped convolution.
        batch_group_count: number of batch groups for grouped convolution.
      Returns: a XlaOp representing the Conv operation.
      """
      pads = _convert_padding_type_to_pad_values(
          padding,
          self.GetShape(lhs).dimensions()[2:],
          self.GetShape(rhs).dimensions()[2:], window_strides)
      return self.ConvGeneralDilated(
          lhs,
          rhs,
          window_strides,
          pads, [], [],
          dimension_numbers=None,
          feature_group_count=feature_group_count,
          batch_group_count=batch_group_count,
          precision_config=precision_config)
function_tokens: def Conv ( self , lhs , rhs , window_strides , padding , feature_group_count = 1 , batch_group_count = 1 , precision_config = None ) : pads = _convert_padding_type_to_pad_values ( padding , self . GetShape ( lhs ) . dimensions ( ) [ 2 : ] , self . GetShape ( rhs ) . dimensions ( ) [ 2 : ] , window_strides ) return self . ConvGeneralDilated ( lhs , rhs , window_strides , pads , [ ] , [ ] , dimension_numbers = None , feature_group_count = feature_group_count , batch_group_count = batch_group_count , precision_config = precision_config )