动手学深度学习pytorch版-填坑-AttributeError: ‘Tensor‘ object has no attribute ‘T‘

在8.5节的循环神经⽹络的从零开始实现中

这个地方封装进行one_hot编码时候会报错- ‘Tensor’ object has no attribute ‘T’
应该是笔误或者版本迭代的问题，实际上pytorch并不支持直接用.T进行张量的转置，常用的方法有
tensor.t() or tensor.transpose(dim1,dim2)
更改后的整个RNN的从零实现的完整代码：

import math
import torch
from torch import nn
from torch.nn import functional as F
from d2l import torch as d2l
import numpy as np
batch_size, num_steps = 32, 35
train_iter, vocab = d2l.load_data_time_machine(batch_size, num_steps)
X = torch.arange(10).reshape((2, 5))
def get_params(vocab_size, num_hiddens, device):
    num_inputs = num_outputs = vocab_size
    def normal(shape):
        return torch.randn(size=shape, device=device) * 0.01
    # 隐藏层参数
    W_xh = normal((num_inputs, num_hiddens))
    W_hh = normal((num_hiddens, num_hiddens))
    b_h = torch.zeros(num_hiddens, device=device)
    # 输出层参数
    W_hq = normal((num_hiddens, num_outputs))
    b_q = torch.zeros(num_outputs, device=device)
    # 附加梯度
    params = [W_xh, W_hh, b_h, W_hq, b_q]
    for param in params:
        param.requires_grad_(True)
    return params

def init_rnn_state(batch_size, num_hiddens, device):
    return (torch.zeros((batch_size, num_hiddens), device=device),)
def rnn(inputs, state, params):
    # `inputs`的形状：(`时间步数量`, `批量⼤⼩`, `词表⼤⼩`)
    W_xh, W_hh, b_h, W_hq, b_q = params
    H, = state
    outputs = []
    # `X`的形状：(`批量⼤⼩`, `词表⼤⼩`)
    for X in inputs:
         H = torch.tanh(torch.mm(X, W_xh) + torch.mm(H, W_hh) + b_h)
         Y = torch.mm(H, W_hq) + b_q
         outputs.append(Y)
    return torch.cat(outputs, dim=0), (H,)
class RNNModelScratch: #@save
    """从零开始实现的循环神经⽹络模型"""
    def __init__(self, vocab_size, num_hiddens, device, get_params, init_state, forward_fn):
        self.vocab_size, self.num_hiddens = vocab_size, num_hiddens
        self.params = get_params(vocab_size, num_hiddens, device)
        self.init_state, self.forward_fn = init_state, forward_fn

    def __call__(self, X, state):
        X = F.one_hot(X.t(), self.vocab_size).type(torch.float32)
        return self.forward_fn(X, state, self.params)
    def begin_state(self, batch_size, device):
        return self.init_state(batch_size, self.num_hiddens, device)

num_hiddens = 512
net = RNNModelScratch(len(vocab), num_hiddens, d2l.try_gpu(), get_params,init_rnn_state, rnn)
state = net.begin_state(X.shape[0],d2l.try_gpu())
Y, new_state = net(X.to(d2l.try_gpu()), state)
print(Y.shape)