docs: def conv2d_fixed_padding(inputs, filters, kernel_size, strides, data_format):
    """Strided 2-D convolution with explicit padding."""
    # The padding is consistent and is based only on `kernel_size`, not on the
    # dimensions of `inputs` (as opposed to using `tf.layers.conv2d` alone).
  
    inputs_for_logging = inputs
    if strides > 1:
      inputs = fixed_padding(inputs, kernel_size, data_format)
  
    outputs = tf.layers.conv2d(
        inputs=inputs, filters=filters, kernel_size=kernel_size, strides=strides,
        padding=('SAME' if strides == 1 else 'VALID'), use_bias=False,
        kernel_initializer=tf.variance_scaling_initializer(
            distribution="truncated_normal"),
        data_format=data_format)
  
    resnet_log_helper.log_conv2d(
        input_tensor=inputs_for_logging, output_tensor=outputs, stride=strides,
        filters=filters, initializer=mlperf_log.TRUNCATED_NORMAL, use_bias=False)
  
    return outputs