}
  FunctionLibraryDefinition fld(OpRegistry::Global(), fdl);
  {
    std::unique_ptr<Graph> g(new Graph(&fld));

    tensorflow::TensorProto tensor_proto;
    tensor_proto.set_dtype(tensorflow::DT_INT32);
    tensorflow::TensorShapeProto shape;
    shape.add_dim()->set_size(2);
    *tensor_proto.mutable_tensor_shape() = shape;
    for (int i = 0; i < 2; ++i) {
      tensor_proto.add_int_val(1);

public String getHost(); public void setHost(String); public String getPassword(); public void setPassword(String); public int getPort(); public void setPort(int); public String getUserName(); public void setUserName(String); public String getType(); public void setType(String); public String getNonProxyHosts(); public void setNonProxyHosts(String); public String getNtlmHost(); public void setNtlmHost(String); public void setNtlmDomain(String); public String getNtlmDomain(); } org/apache/maven/wagon/...

  validate_indices:bool;
}

table EqualOptions {
}

table NotEqualOptions {
}

table ShapeOptions {
  // Optional output type of the operation (int32 or int64). Defaults to int32.
  out_type : TensorType;
}

table RankOptions {
}

table PowOptions {
}

table FakeQuantOptions {
  // Parameters supported by version 1:
  min:float;
  max:float;
  num_bits:int;

  @Test
  fun multipartBodyBuilder() {
    val requestBody = "".toRequestBody(null)
    var builder: MultipartBody.Builder = MultipartBody.Builder()
    builder = MultipartBody.Builder("")
    builder = builder.setType("".toMediaType())
    builder = builder.addPart(requestBody)
    builder = builder.addPart(headersOf(), requestBody)
    builder = builder.addPart(null, requestBody)
    builder = builder.addFormDataPart("", "")

                                               /*type=*/2);
  auto send = builder.create<SendOp>(
      loc, token.getType(), operand, token, channel_handle,
      /*is_host_transfer=*/builder.getBoolAttr(true));

  SetFrontendAttributes(send, index, key, operand.getType(),
                        /*device_to_host=*/true, host_handler_name);

  SetOpSharding(send, manual_sharding);

public String getHost(); public void setHost(String); public String getPassword(); public void setPassword(String); public int getPort(); public void setPort(int); public String getUserName(); public void setUserName(String); public String getType(); public void setType(String); public String getNonProxyHosts(); public void setNonProxyHosts(String); public String getNtlmHost(); public void setNtlmHost(String); public void setNtlmDomain(String); public String getNtlmDomain(); } org/apache/maven/wagon/...

			if t.IsSigned() && constant.Compare(cv, token.GEQ, constant.MakeUint64(1<<(bits-1))) {
				cv = constant.BinaryOp(cv, token.OR, constant.MakeInt64(-1<<(bits-1)))
			}
		}
	}
	c = ir.NewConstExpr(cv, c)
	c.SetType(t)
	return c, true
}

func addStr(n *ir.AddStringExpr) ir.Node {
	// Merge adjacent constants in the argument list.
	s := n.List
	need := 0
	for i := 0; i < len(s); i++ {

}

table EqualOptions {
}

table NotEqualOptions {
}

table ShapeOptions {
  // Optional output type of the operation (int32 or int64). Defaults to int32.
  out_type : TensorType;
}

table RankOptions {
}

table PowOptions {
}

table FakeQuantOptions {
  // Parameters supported by version 1:
  min:float;
  max:float;

                         const TensorShapeProto& shape, Graph* graph_out) {
  DataType data_type = src_node->output_type(src_port);
  TensorProto dummy_proto;
  dummy_proto.set_dtype(data_type);
  *dummy_proto.mutable_tensor_shape() = shape;
  // Don't set any value field in the proto, since it is only going to be used
  // for shape inference.

        int type = be.getOperation().getType();
        if ((type == Types.ASSIGNMENT_OPERATOR || type == Types.EQUAL)
            && left instanceof ClassExpression) {
            ClassExpression ce = (ClassExpression) left;
            String error = "you tried to assign a value to the class '" + ce.getType().getName() + "'";
            if (ce.getType().isScript()) {