code: def build_conv1d_block(
      block: Dict[str, Any], block_type: str
  ) -> CausalConv1d:
      """Build function for causal conv1d block.
  
      Args:
          block: CausalConv1d or Conv1D block parameters.
  
      Returns:
          : Function to create conv1d (encoder) or causal conv1d (decoder) block.
  
      """
      if block_type == "conv1d":
          conv_class = Conv1d
      else:
          conv_class = CausalConv1d
  
      stride = block.get("stride", 1)
      dilation = block.get("dilation", 1)
      groups = block.get("groups", 1)
      bias = block.get("bias", True)
  
      use_batch_norm = block.get("use-batch-norm", False)
      use_relu = block.get("use-relu", False)
      dropout_rate = block.get("dropout-rate", 0.0)
  
      return lambda: conv_class(
          block["idim"],
          block["odim"],
          block["kernel_size"],
          stride=stride,
          dilation=dilation,
          groups=groups,
          bias=bias,
          relu=use_relu,
          batch_norm=use_batch_norm,
          dropout_rate=dropout_rate,
      )
  
imports: from typing import Any, Dict, List, Tuple, Union
  import torch
  from espnet.nets.pytorch_backend.conformer.convolution import ConvolutionModule
  from espnet.nets.pytorch_backend.conformer.encoder_layer import EncoderLayer
  from espnet.nets.pytorch_backend.nets_utils import get_activation
  from espnet.nets.pytorch_backend.transducer.conv1d_nets import CausalConv1d, Conv1d
  from espnet.nets.pytorch_backend.transducer.transformer_decoder_layer import TransformerDecoderLayer
  from espnet.nets.pytorch_backend.transducer.vgg2l import VGG2L
  from espnet.nets.pytorch_backend.transformer.attention import MultiHeadedAttention, RelPositionMultiHeadedAttention
  from espnet.nets.pytorch_backend.transformer.embedding import PositionalEncoding, RelPositionalEncoding, ScaledPositionalEncoding
  from espnet.nets.pytorch_backend.transformer.encoder_layer import EncoderLayer
  from espnet.nets.pytorch_backend.transformer.positionwise_feed_forward import PositionwiseFeedForward
  from espnet.nets.pytorch_backend.transformer.repeat import MultiSequential
  from espnet.nets.pytorch_backend.transformer.subsampling import Conv2dSubsampling
label_desc: Use image node_id 8 for calling a global function with example usage: build_conv1d_block(block, block_type) and returns: function