[[["Easy to understand","easyToUnderstand","thumb-up"],["Solved my problem","solvedMyProblem","thumb-up"],["Other","otherUp","thumb-up"]],[["Missing the information I need","missingTheInformationINeed","thumb-down"],["Too complicated / too many steps","tooComplicatedTooManySteps","thumb-down"],["Out of date","outOfDate","thumb-down"],["Samples / code issue","samplesCodeIssue","thumb-down"],["Other","otherDown","thumb-down"]],["Last updated 2024-04-26 UTC."],[],[],null,["# tfma.utils.merge_extracts\n\n\u003cbr /\u003e\n\n|--------------------------------------------------------------------------------------------------------------------------------------|\n| [View source on GitHub](https://github.com/tensorflow/model-analysis/blob/v0.46.0/tensorflow_model_analysis/utils/util.py#L782-L885) |\n\nMerges list of extracts into a single extract with multidimensional data. \n\n tfma.utils.merge_extracts(\n extracts: List[../../tfma/types/Extracts],\n squeeze_two_dim_vector: bool = True\n ) -\u003e ../../tfma/types/Extracts\n\n| **Note:** Running split_extracts followed by merge extracts with default options will not reproduce the exact shape of the original extracts. Arrays in shape (x,1) will be flattened to (x,). To maintain the original shape of extract values of array shape (x,1), you must run with these options: split_extracts(extracts, expand_zero_dims=False) merge_extracts(extracts, squeeze_two_dim_vector=False) Args: extracts: Batched TFMA Extracts. squeeze_two_dim_vector: Determines how the function will handle arrays of shape (x,1). If squeeze_two_dim_vector is True, the array will be squeezed to shape (x,).\n\n\u003cbr /\u003e\n\n\u003cbr /\u003e\n\n\u003cbr /\u003e\n\n| Returns ------- ||\n|---|---|\n| A single Extracts whose values have been grouped into batches. ||\n\n\u003cbr /\u003e"]]
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