// Returns a map from variable names to their shapes.  Slices of a partitioned
  // tensor are combined into a single entry.
  const TensorSliceReader::VarToShapeMap& GetVariableToShapeMap() const;

  // Returns a map from variable names to their data types.  Slices of a
  // partitioned tensor are combined into a single entry.
  const TensorSliceReader::VarToDataTypeMap& GetVariableToDataTypeMap() const;

    TF_SetStatus(status, TF_OK, "");
    return 0;
  }
  if (!IsCacheEnabled() || (n > max_bytes_)) {
    // The cache is effectively disabled, so we pass the read through to the
    // fetcher without breaking it up into blocks.
    return block_fetcher_(filename, offset, n, buffer, status);
  }
  // Calculate the block-aligned start and end of the read.
  size_t start = block_size_ * (offset / block_size_);

    see! value: render: shell validations: required: true

-   type: textarea id: logs attributes: label: Relevant log output description:
    Please copy and paste any relevant log output. This will be automatically

to the `../wheel_test/` directory.


### How it Works in the Presubmit Job

`_requirements_lock` files will be generated by the presubmit job. A detailed
description will be provided once it's integrated into presubmit.

### test_import_api_packages

This Python test verifies whether the API v2 packages can be imported from the
current build. It utilizes the `_api/v2/api_packages.txt` list of packages from

      TestScalarTensorHandle<T, datatype>(eager_ctx, value);
  *tensor =
      unwrap(TF_CreateAbstractTensorFromEagerTensor(input_eager, status.get()));
  return absl::OkStatus();
}

// Places data from `t` into *result_tensor.
Status GetValue(AbstractTensorHandle* t, TF_Tensor** result_tensor);
}  // namespace tensorflow

# "pip tests" run a similar suite of tests the "nonpip" tests, but do something
# odd to attempt to validate the quality of the pip package. The wheel is
# installed into a virtual environment, and then that venv is used to run all
# bazel tests with a special flag "--define=no_tensorflow_py_deps=true", which
# drops all the bazel dependencies for each py_test; this makes all the tests

  ASSERT_EQ(TF_OK, TF_GetCode(status.get())) << TF_Message(status.get());
  TF_Tensor* result_tensor = TFE_TensorHandleResolve(result_t, status.get());

  // Copy Tensor data into an array.
  float result_data[4] = {0};
  memcpy(&result_data[0], TF_TensorData(result_tensor),
         TF_TensorByteSize(result_tensor));

  int data_len = 4;  // length of result_data

  env->SetNowSeconds(now);
  tf_gcs_filesystem::RamFileBlockCache cache(
      8, 32, 1 /* max staleness */, fetcher,
      [&env]() { return env->NowSeconds(); });
  // Read three blocks into the cache, and advance the timestamp by one second
  // with each read. Start with a block of "a" at the current timestamp `now`.
  TF_EXPECT_OK(ReadCache(&cache, "a", 0, 1, &out));

  EXPECT_EQ(0, TF_OperationNumControlOutputs(neg));
  EXPECT_EQ(0, TF_OperationGetControlOutputs(neg, control_ops, 100));

  // Import it again, with an input mapping, return outputs, and a return
  // operation, into the same graph.
  TF_DeleteImportGraphDefOptions(opts);
  opts = TF_NewImportGraphDefOptions();
  TF_ImportGraphDefOptionsSetPrefix(opts, "imported2");
  TF_ImportGraphDefOptionsAddInputMapping(opts, "scalar", 0, {scalar, 0});

  TF_DeleteTensor(t);
  EXPECT_EQ(2, product[0]);
  EXPECT_EQ(4, product[1]);
  EXPECT_EQ(6, product[2]);
  EXPECT_EQ(8, product[3]);

  // When we turn on grappler, confirm that the tf.Add has been rewritten into a
  // tf.Mul.
  // This capability of checking the executed op names is currently only enabled
  // for TFRT debug build, for performance and simplicity reasons.
  if (use_tfrt) {