!9058 Fix MultiFieldEmbedding Doc Error

From: @huangxinjing
Reviewed-by: @stsuteng,@zh_qh
Signed-off-by: @stsuteng

mindspore/nn/layer/embedding.py

@@ -273,7 +273,7 @@ class EmbeddingLookup(Cell):
 
 class MultiFieldEmbeddingLookup(EmbeddingLookup):
     r"""
-    Returns a slice of input tensor based on the specified indices based on the filed ids. This operation
+    Returns a slice of input tensor based on the specified indices based on the field ids. This operation
     supports looking up embeddings within multi hot and one hot fields simultaneously.
 
     Note:
@@ -284,7 +284,7 @@ class MultiFieldEmbeddingLookup(EmbeddingLookup):
         specified 'axis = 0' to lookup table.
         The vectors with the same field_ids  will be combined by the `operator`, such as `SUM`, `MAX` and
         `MEAN`. Ensure the input_values of the padded id is zero, so that they can be ignored. The final
-        output will be zeros if the summed of absolute weight of the field is zero. This class only
+        output will be zeros if the sum of absolute weight of the field is zero. This class only
         supports ['table_row_slice', 'batch_slice' and 'table_column_slice']
 
     Args:
@@ -300,29 +300,31 @@ class MultiFieldEmbeddingLookup(EmbeddingLookup):
         max_norm (Union[float, None]): A maximum clipping value. The data type must be float16, float32
                                        or None. Default: None
         sparse (bool): Using sparse mode. When 'target' is set to 'CPU', 'sparse' has to be true. Default: True.
-        operator (string): The pooling method for the features in one field. Support `SUM`, `MEAN` and 'MAX'
+        operator (string): The pooling method for the features in one field. Support 'SUM, 'MEAN' and 'MAX'
 
     Inputs:
         - **input_indices** (Tensor) - The shape of tensor is :math:`(batch_size, seq_length)`.
-          Specifies the indices of elements of the original Tensor. Values can be out of range of embedding_table,
-          and the exceeding part will be filled with 0 in the output. Input_indices must be a 2d tensor in
+          Specifies the indices of elements of the original Tensor. Input_indices must be a 2d tensor in
           this interface. Type is Int16, Int32, Int64.
         - **input_values** (Tensor) - The shape of tensor is :math:`(batch_size, seq_length)`.
           Specifies the weights of elements of the input_indices. The lookout vector will multiply with
           the input_values. Type is Float32.
         - **field_ids** (Tensor)  - The shape of tensor is :math:`(batch_size, seq_length)`.
-          Specifics the field id of elements of the input_indices. Type is Type is Int16, Int32, Int64.
+          Specifies the field id of elements of the input_indices. Type is Type is Int16, Int32.
 
     Outputs:
         Tensor, the shape of tensor is :math:`(batch_size, field_size, embedding_size)`. Type is Float32.
 
+    Supported Platforms:
+        ``Ascend`` ``GPU``
+
     Examples:
         >>> input_indices = Tensor([[2, 4, 6, 0, 0], [1, 3, 5, 0, 0]], mindspore.int32)
         >>> input_values = Tensor([[1, 1, 1, 0, 0], [1, 1, 1, 0, 0]], mindspore.float32)
         >>> field_ids = Tensor([[0, 1, 1, 0, 0], [0, 0, 1, 0, 0]], mindspore.int32)
         >>> net = nn.MultiFieldEmbeddingLookup(10, 2, field_size=2, operator='SUM')
         >>> out = net(input_indices, input_values, field_ids)
-        >>> print(result)
+        >>> print(out)
         [[[-0.00478983 -0.00772568]
           [-0.00968955 -0.00064902]]
          [[-0.01251151 -0.01251151]
@@ -335,7 +337,7 @@ class MultiFieldEmbeddingLookup(EmbeddingLookup):
                  slice_mode='batch_slice', feature_num_list=None, max_norm=None, sparse=True, operator='SUM'):
         super(MultiFieldEmbeddingLookup, self).__init__(vocab_size, embedding_size, param_init, target,
                                                         slice_mode, feature_num_list, max_norm, sparse)
-        self.field_size = field_size
+        self.field_size = validator.check_value_type('field_size', field_size, [int], self.cls_name)
         self.operator = operator
 
         self.mul = P.Mul()