class_docstr: Defines a datatype together with instructions for converting to Tensor.
  
  Field class models common text processing datatypes that can be represented
  by tensors.  It holds a Vocab object that defines the set of possible values
  for elements of the field and their corresponding numerical representations.
  The Field object also holds other parameters relating to how a datatype
  should be numericalized, such as a tokenization method and the kind of
  Tensor that should be produced.
  
  If a Field is shared between two columns in a dataset (e.g., question and
  answer in a QA dataset), then they will have a shared vocabulary.
  
  Attributes:
      sequential: Whether the datatype represents sequential data. If False,
          no tokenization is applied. Default: True.
      use_vocab: Whether to use a Vocab object. If False, the data in this
          field should already be numerical. Default: True.
      init_token: A token that will be prepended to every example using this
          field, or None for no initial token. Default: None.
      eos_token: A token that will be appended to every example using this
          field, or None for no end-of-sentence token. Default: None.
      fix_length: A fixed length that all examples using this field will be
          padded to, or None for flexible sequence lengths. Default: None.
      dtype: The torch.dtype class that represents a batch of examples
          of this kind of data. Default: torch.long.
      preprocessing: The Pipeline that will be applied to examples
          using this field after tokenizing but before numericalizing. Many
          Datasets replace this attribute with a custom preprocessor.
          Default: None.
      postprocessing: A Pipeline that will be applied to examples using
          this field after numericalizing but before the numbers are turned
          into a Tensor. The pipeline function takes the batch as a list, and
          the field's Vocab.
          Default: None.
      lower: Whether to lowercase the text in this field. Default: False.
      tokenize: The function used to tokenize strings using this field into
          sequential examples. If "spacy", the SpaCy tokenizer is
          used. If a non-serializable function is passed as an argument,
          the field will not be able to be serialized. Default: string.split.
      tokenizer_language: The language of the tokenizer to be constructed.
          Various languages currently supported only in SpaCy.
      include_lengths: Whether to return a tuple of a padded minibatch and
          a list containing the lengths of each examples, or just a padded
          minibatch. Default: False.
      batch_first: Whether to produce tensors with the batch dimension first.
          Default: False.
      pad_token: The string token used as padding. Default: "<pad>".
      unk_token: The string token used to represent OOV words. Default: "<unk>".
      pad_first: Do the padding of the sequence at the beginning. Default: False.
      truncate_first: Do the truncating of the sequence at the beginning. Default: False
      stop_words: Tokens to discard during the preprocessing step. Default: None
      is_target: Whether this field is a target variable.
          Affects iteration over batches. Default: False
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code: def __hash__(self):
      # we don't expect this to be called often
      return 42
  
imports: from collections import Counter, OrderedDict
  from itertools import chain
  import torch
  from torchtext.data.utils import dtype_to_attr, get_tokenizer, is_tokenizer_serializable
  from ..vocab import Vocab
  from .dataset import Dataset
  from .pipeline import Pipeline
label_desc: Use image node_id 4 for calling the Field obj's underlying member method code with example usage: obj.__hash__() and returns: int