code: def TranslateCrop(layer, pretrained_blobs, is_test, **kwargs):
      net, net_params, input_dims = (
          kwargs["net"],
          kwargs["net_params"],
          kwargs["input_dims"],
      )
      n, c, h, w = input_dims
      dummy_input = np.random.randn(n, c, h, w).astype(np.float32)
      dim_map = _GetBlobDimMap(net, net_params, dummy_input)
      param = layer.crop_param
      axis, offsets = param.axis, param.offset
      caffe_op = BaseTranslate(layer, "Slice")
      input_1 = caffe_op.input[1]
      input_1_dim = dim_map[input_1]
      starts, ends = [], []
      dims = len(dim_map[input_1])
      assert (
          len(offsets) == 1
      ), "Caffe Translator for Crop only works for offset \
      of 1 for now"
      for _ in range(axis):
          starts.append(0)
          ends.append(-1)
      end_offset = [
          int(offsets[0] + input_1_dim[i]) for i in range(axis, dims)
      ]
      ends.extend(end_offset)
      starts.extend([offsets[0]] * len(end_offset))
      op = caffe2_pb2.OperatorDef()
      op.input.extend([caffe_op.input[0]])
      op.output.extend(caffe_op.output)
      op.arg.extend(caffe_op.arg)
      op.type = caffe_op.type
      AddArgument(op, "starts", starts)
      AddArgument(op, "ends", ends)
      return op, []
  
imports: import argparse
  import copy
  import logging
  import re
  import numpy
  from caffe2.proto import caffe2_pb2, caffe2_legacy_pb2
  from caffe.proto import caffe_pb2
  from caffe2.python import core, utils, workspace
  from google.protobuf import text_format
label_desc: Use image node_id 20 for calling a global function with example usage: TranslateCrop(layer, pretrained_blobs, is_test) and returns: op,  list