class_docstr: A `TextFieldEmbedder` is a `Module` that takes as input the
  [`DataArray`](../../data/fields/text_field.md) produced by a [`TextField`](../../data/fields/text_field.md) and
  returns as output an embedded representation of the tokens in that field.
  
  The `DataArrays` produced by `TextFields` are _dictionaries_ with named representations, like
  "words" and "characters".  When you create a `TextField`, you pass in a dictionary of
  [`TokenIndexer`](../../data/token_indexers/token_indexer.md) objects, telling the field how exactly the
  tokens in the field should be represented.  This class changes the type signature of `Module.forward`,
  restricting `TextFieldEmbedders` to take inputs corresponding to a single `TextField`, which is
  a dictionary of tensors with the same names as were passed to the `TextField`.
  
  We also add a method to the basic `Module` API: `get_output_dim()`.  You might need this
  if you want to construct a `Linear` layer using the output of this embedder, for instance.
docstr: Returns the dimension of the vector representing each token in the output of this
  `TextFieldEmbedder`.  This is _not_ the shape of the returned tensor, but the last element
  of that shape.
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code: def get_output_dim(self) -> int:
      """
      Returns the dimension of the vector representing each token in the output of this
      `TextFieldEmbedder`.  This is _not_ the shape of the returned tensor, but the last element
      of that shape.
      """
      raise NotImplementedError
  
file_path: allennlp/allennlp/modules/text_field_embedders/text_field_embedder.py
imports: import torch
  from allennlp.common.registrable import Registrable
  from allennlp.data.fields.text_field import TextFieldTensors
label_desc: Use image node_id 2 for calling the TextFieldEmbedder obj's underlying member method code with example usage: obj.get_output_dim() without return types