code: def _fix_dcu_input_dims(fix_length_mode, x, encoders_stride_product):
      """Pad or trim `x` to a length compatible with DCUNet."""
      freq_prod = int(encoders_stride_product[0])
      time_prod = int(encoders_stride_product[1])
      if (x.shape[2] - 1) % freq_prod:
          raise TypeError(
              f"Input shape must be [batch, ch, freq + 1, time + 1] "
              f"with freq divisible by "
              f"{freq_prod}, got {x.shape} instead"
          )
      time_remainder = (x.shape[3] - 1) % time_prod
      if time_remainder:
          if fix_length_mode is None:
              raise TypeError(
                  f"Input shape must be [batch, ch, freq + 1, time + 1] with time "
                  f"divisible by {time_prod}, got {x.shape} instead."
                  f" Set the 'fix_length_mode' argument "
                  f"in 'DCUNet' to 'pad' or 'trim' to fix shapes automatically."
              )
          elif fix_length_mode == "pad":
              pad_shape = [0, time_prod - time_remainder]
              x = nn.functional.pad(x, pad_shape, mode="constant")
          elif fix_length_mode == "trim":
              pad_shape = [0, -time_remainder]
              x = nn.functional.pad(x, pad_shape, mode="constant")
          else:
              raise ValueError(
                  f"Unknown fix_length mode '{fix_length_mode}'"
              )
      return x
  
imports: import functools
  from functools import partial
  import numpy
  import torch
  from torch import Tensor, nn
  from torch.nn.modules.batchnorm import _BatchNorm
label_desc: Use image node_id 5 for calling a global function with example usage: _fix_dcu_input_dims(fix_length_mode, x, encoders_stride_product) and returns: x