code: def build_conv1d_block(
      configuration: List[Dict[str, Any]],
      causal: bool,
  ) -> Conv1d:
      """Build Conv1d block.
  
      Args:
          configuration: Conv1d block configuration.
  
      Returns:
          : Conv1d block function.
  
      """
      return lambda: Conv1d(
          configuration["input_size"],
          configuration["output_size"],
          configuration["kernel_size"],
          stride=configuration.get("stride", 1),
          dilation=configuration.get("dilation", 1),
          groups=configuration.get("groups", 1),
          bias=configuration.get("bias", True),
          relu=configuration.get("relu", True),
          batch_norm=configuration.get("batch_norm", False),
          causal=causal,
          dropout_rate=configuration.get("dropout_rate", 0.0),
      )
  
imports: from typing import Any, Dict, List, Optional, Union
  from espnet2.asr_transducer.activation import get_activation
  from espnet2.asr_transducer.encoder.blocks.branchformer import Branchformer
  from espnet2.asr_transducer.encoder.blocks.conformer import Conformer
  from espnet2.asr_transducer.encoder.blocks.conv1d import Conv1d
  from espnet2.asr_transducer.encoder.blocks.conv_input import ConvInput
  from espnet2.asr_transducer.encoder.blocks.ebranchformer import EBranchformer
  from espnet2.asr_transducer.encoder.modules.attention import RelPositionMultiHeadedAttention
  from espnet2.asr_transducer.encoder.modules.convolution import ConformerConvolution, ConvolutionalSpatialGatingUnit, DepthwiseConvolution
  from espnet2.asr_transducer.encoder.modules.multi_blocks import MultiBlocks
  from espnet2.asr_transducer.encoder.modules.positional_encoding import RelPositionalEncoding
  from espnet2.asr_transducer.normalization import get_normalization
  from espnet.nets.pytorch_backend.transformer.positionwise_feed_forward import PositionwiseFeedForward
label_desc: Use image node_id 6 for calling a global function with example usage: build_conv1d_block(configuration, causal) and returns: function