code: def build_input_block(
      input_size: int,
      configuration: Dict[str, Union[str, int]],
  ) -> ConvInput:
      """Build encoder input block.
  
      Args:
          input_size: Input size.
          configuration: Input block configuration.
  
      Returns:
          : ConvInput block function.
  
      """
      return ConvInput(
          input_size,
          configuration["conv_size"],
          configuration["subsampling_factor"],
          vgg_like=configuration["vgg_like"],
          output_size=configuration["output_size"],
      )
  
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 3 for calling a global function with example usage: build_input_block(input_size, configuration) and returns: ConvInput