SmallVector<int64_t, 4> permutation_array(updates_type.getRank());
  int64_t dim = 0;
  // Move leading indices to the back of the array.
  const auto permutation_array_size = permutation_array.size();
  for (int64_t i = update_window_dims.size(); i < permutation_array_size; ++i) {
    permutation_array[i] = dim;
    ++dim;
  }
  // Move trailing indices to the front of the array.

  op: "Placeholder"
  attr {
    key: "dtype"
    value {
      type: DT_FLOAT
    }
  }
  attr {
    key: "shape"
    value {
      shape {
        dim {
          size: 2
        }
        dim {
          size: 5
        }
        dim {
          size: 3
        }
      }
    }
  }
}
node {
  name: "Placeholder_1"
  op: "Placeholder"
  attr {
    key: "dtype"
    value {

        dtype: DT_FLOAT
        tensor_shape {
          dim {
          }
        }
      }
    }
  }
}
node {
  name: "ParseExample/Const_1"
  op: "Const"
  attr {
    key: "dtype"
    value {
      type: DT_FLOAT
    }
  }
  attr {
    key: "value"
    value {
      tensor {
        dtype: DT_FLOAT
        tensor_shape {
          dim {
          }
        }
      }
    }
  }

      tensor {
        dtype: DT_INT64
        tensor_shape {
          dim {
            size: 2
          }
        }
        tensor_content: "\x00\x00\x00\x00\x00\x00\x00\x00\x08\x00\x00\x00\x00\x00\x00\x00"
      }
    }
  }
  attr {
    key: "_output_shapes"
    value {
      list {
        shape {
          dim {
            size: 2
          }
        }
      }
    }
  }

  tensorflow::string s = "[";
  for (int i = 0; i < TF_NumDims(tensor); ++i) {
    if (i > 0) tensorflow::strings::StrAppend(&s, ",");
    int64_t dim = TF_Dim(tensor, i);
    // A TF_Tensor cannot have an unknown dimension.
    CHECK_GE(dim, 0);
    tensorflow::strings::StrAppend(&s, dim);
  }
  tensorflow::strings::StrAppend(&s, "]");
  return s;
}

# Text form of tensorflow.tf2xla.Config proto.
feed {
  id { node_name: "x_const" }
  shape {
    dim { size: 1 }
  }
}
feed {
  id { node_name: "y_const" }
  shape {
    dim { size: 1 }
  }
}
fetch {
  id { node_name: "x_y_sum" }

# Text form of tensorflow.tf2xla.Config proto.
feed {
  id { node_name: "params" }
  shape {
    dim { size: 4 }
  }
}
feed {
  id { node_name: "indices" }
  shape {
    dim { size: 2 }
  }
}
fetch {
  id { node_name: "gather_output" }

  llvm::SmallVector<bool, 4> is_reduce_dim(rank, false);
  for (const APInt &index : indices.getValues<APInt>()) {
    int64_t dim = GetDimForAxis(index.getSExtValue(), rank);
    // Invalid input.
    if (dim < 0 || dim >= rank) return UnrankedTensorType::get(element_ty);

    if (!is_reduce_dim[dim]) {
      is_reduce_dim[dim] = true;
      num_reduce_dim++;
    }
  }

  ArrayRef<int64_t> shape = ranked_ty.getShape();

# Text form of tensorflow.tf2xla.Config proto.
feed {
  id { node_name: "p_hold" }
  shape {}
}
feed {
  id { node_name: "x_hold" }
  shape {
    dim { size: 1 }
  }
}
feed {
  id { node_name: "y_hold" }
  shape {
    dim { size: 1 }
  }
}
fetch {
  id { node_name: "result" }

  op: "Const"
  attr {
    key: "dtype"
    value {
      type: DT_STRING
    }
  }
  attr {
    key: "value"
    value {
      tensor {
        dtype: DT_STRING
        tensor_shape {
          dim {
            size: 1
          }
        }
        string_val: ""
      }
    }
  }
}
node {
  name: "Const"
  op: "Const"
  attr {
    key: "dtype"
    value {
      type: DT_STRING